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GitHub

Code reference

In this page we document the functions which constitute the bulk of BeforeIT.jl functionality.

Agent types

BeforeIT.Aggregates — Type

This is a Aggregates type. It is used to store the aggregate variables of the economy. Note that t is an integer, while the rest are floats or vectors of floats.

Fields

  • Y [vector]: GDP data + predictions
  • pi_ [vector]: inflation data + predictions
  • P_bar: Global price index
  • P_bar_g [vector]: Producer price index for principal good g
  • P_bar_HH: Consumer price index
  • P_bar_CF: Capital price index
  • P_bar_h: CPI_h
  • P_bar_CF_h: Capital price index _h
  • Y_e: Expected GDP
  • gamma_e: Expected growth
  • pi_e: Expected inflation
  • t: Time index
source
BeforeIT.Bank — Type

This is a Bank type. It represents the bank of the model.

Fields

  • E_k: equity capital (common equity) of the bank
  • Pi_k: Profits of the bank
  • Pi_e_k: Expected profits of the bank
  • D_k: Residual and balancing item on the bank’s balance sheet
  • r: Rate for loans and morgages

Household fields (bank' owner)

  • Y_h: Net disposable income of bank owner (investor)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
  • K_h: Capital stock
  • D_h: Deposits
source
BeforeIT.CentralBank — Type

This is a CentralBank type. It represents the central bank of the model.

Fields

  • r_bar: Nominal interest rate
  • r_G: Interest rate on government bonds
  • rho: Parameter for gradual adjustment of the policy rate
  • r_star: Real equilibrium interest rate
  • pi_star: Inflation target by CB
  • xi_pi: Weight the CB puts on inflation targeting
  • xi_gamma: Weight placed on economic
  • E_CB: Central bank equity
source
BeforeIT.Firms — Type

This is a Firms type. Each field is an array which stores the values for all the firms in the economy. Note that the G_i, N_i and V_i fields are integers, while the rest are floats.

For all fields the entry at index i corresponds to the ith firm.

Fields

  • G_i: Principal product
  • alpha_bar_i: Average productivity of labor
  • beta_i: Productivity of intermediate consumption
  • kappa_i: Productivity of capital
  • w_i: Wages
  • w_bar_i: Average wage rate
  • delta_i: Depreciation rate for capital
  • tau_Y_i: Net tax rate on products
  • tau_K_i: Net tax rate on production
  • N_i: Number of persons employed
  • Y_i: Production of goods
  • Q_i: Sales of goods
  • Q_d_i: Demand for goods
  • P_i: Price
  • S_i: Inventories
  • K_i: Capital, in real terms
  • M_i: Intermediate goods/services and raw materials, in real terms
  • L_i: Outstanding loans
  • pi_bar_i: Operating margin
  • D_i: Deposits of the firm
  • Pi_i: Profits
  • V_i: Vacancies
  • I_i: Investments
  • E_i: Equity
  • P_bar_i: Price index
  • P_CF_i: Price index
  • DS_i: Differnece in stock of final goods
  • DM_i: Difference in stock of intermediate goods
  • DL_i: Obtained loans
  • DL_d_i: Target loans
  • K_e_i: Expected capital
  • L_e_i: Expected loans
  • Q_s_i: Expected sales
  • I_d_i: Desired investments
  • DM_d_i: Desired materials
  • N_d_i: Desired employment
  • Pi_e_i: Expected profits

Household fields (firms' owners)

  • Y_h: Net disposable income of firm owner (investor)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
  • K_h: Capital stock
  • D_h: Deposits of the owner of the firms
source
BeforeIT.Government — Type

This is a Government type. It represents the government of the model.

Fields

  • alpha_G: Autoregressive coefficient for government consumption
  • beta_G: Scalar constant for government consumption
  • sigma_G: Variance coefficient for government consumption
  • Y_G: Government revenues
  • C_G: Consumption demand of the general government
  • L_G: Loans taken out by the government
  • sb_inact: Social benefits for inactive persons
  • sb_other: Social benefits for all
  • C_d_j [vector]: Local governments consumption demand
  • C_j: Realised government consumption
  • P_j: Price inflation of government goods <- ??
source
BeforeIT.Model — Type

This is a Model type. It is used to store all the agents of the economy.

Fields

  • w_act: Workers that are active
  • w_inact: Workers that are inactive
  • firms: Firms
  • bank: Bank
  • cb: CentralBank
  • gov: Government
  • rotw: RestOfTheWorld
  • agg: Aggregates
source
BeforeIT.RestOfTheWorld — Type

This is a RestOfTheWorld type. It represents the rest of the world of the model.

Fields

  • alpha_E: Autoregressive coefficient for exports
  • beta_E: Scalar constant for exports
  • sigma_E: Variance coefficient for exports
  • alpha_I: Autoregressive coefficient for imports
  • beta_I: Scalar constant for imports
  • sigma_I: Variance coefficient for imports
  • Y_EA: GDP euro area
  • gamma_EA: Growth euro area
  • pi_EA: Inflation euro area
  • alpha_pi_EA: Autoregressive coefficient for euro area inflation
  • beta_pi_EA: Autoregressive coefficient for euro area inflation Scalar constant for euro area inflation
  • sigma_pi_EA: Variance coefficient for euro area inflation
  • alpha_Y_EA: Autoregressive coefficient for euro area GDP
  • beta_Y_EA: Autoregressive coefficient for euro area GDP Scalar constant for euro area GDP
  • sigma_Y_EA: Variance coefficient for euro area GDP
  • D_RoW: Net creditor/debtor position of the national economy to the rest of the world
  • Y_I: Supply of imports (in real terms)
  • C_E: Total demand for exports
  • C_d_l [vector]: Demand for exports of specific product
  • C_l: Realised consumption by foreign consumers
  • Y_m [vector]: Supply of imports per sector
  • Q_m [vector]: Sales for imports per sector
  • Q_d_m [vector]: Demand for goods
  • P_m [vector]: Price of imports per sector
  • P_l: Price inflation of exports <- ??
source
BeforeIT.Workers — Type

This is a Workers. Each field is an array which stores the values for all the workers in the economy. Note that the O_h field is an integer, while the rest are floats.

For all fields the entry at index i corresponds to the ith worker.

Fields

  • Y_h: Net disposable income of worker owner (investor)
  • D_h: Deposits
  • K_h: Capital stock
  • w_h: Wages (0 if inactive or unemployed)
  • O_h: Occupation (0 if unemployed, -1 if inactive)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
source

Initialisation function

BeforeIT.init_model — Function
init_model(parameters, initial_conditions, T, typeInt = Int64, typeFloat = Float64)

Initializes the model with given parameters and initial conditions.

Parameters:

  • parameters: A dictionary containing the model parameters.
  • initial_conditions: A dictionary containing the initial conditions.
  • T (integer): The time horizon of the model.
  • typeInt: (optional, default: Int64): The data type to be used for integer values.
  • typeFloat: (optional, default: Float64): The data type to be used for floating-point values.

Returns:

  • model::Model: The initialized model.
source
BeforeIT.update_variables_with_totals! — Method
update_variables_with_totals!(model::Model)

Update the variables in the given model with some global quantities obtained from all agents. This is the last step in the initialization process and it must be performed after all agents have been initialized.

Arguments

  • model::Model: The model object to update.

Returns

  • Nothing
source

Functions to run an entire simulation

BeforeIT.run_one_epoch! — Method
run_one_epoch!(model; multi_threading = false)

This function simulates a single epoch the economic model, updating various components of the model based the interactions between different economic agents. It accepts a model object, which encapsulates the state for the simulation, and an optional boolean parameter multi_threading to enable or disable multi-threading.

Key operations performed include:

  • Financial adjustments for firms and banks, including insolvency checks and profit calculations.
  • Economic expectations and adjustments, such as growth, inflation, and central bank rates.
  • Labor and credit market operations, including wage updates and loan processing.
  • Household economic activities, including consumption and investment budgeting.
  • Government and international trade financial activities, including budgeting and trade balances.
  • General market matching and accounting updates to reflect changes in economic indicators and positions.

The function updates the model in-place and does not return any value.

source
BeforeIT.run_n_sims — Method
run_n_sims(model, n_sims; shock = NoShock(), multi_threading = true)

A function that runs n_sims simulations in parallel with multiple threading and returns a vector of data objects of dimension n_sims.

Arguments

  • model: The model configuration used to simulate.
  • n_sims: The number of simulations to run in parallel.

Returns

  • data_vector: A vector containing the data objects collected during each simulation.
source
BeforeIT.run_one_sim! — Method
run_one_sim!(model; shock = NoShock())

Run a single simulation based on the provided model. The simulation runs for a number of epochs specified by model.prop.T.

Arguments

  • model::Model: The model configuration used for the simulation.

Returns

  • data::Data: The data collected during the simulation.

Details

The function initializes the data using BeforeIT.init_data(model), then iteratively updates the model and data for each epoch using BeforeIT.run_one_epoch!(model) and BeforeIT.update_data!(data, model) respectively.

Example

```julia model = BeforeIT.initializemodel(parameters, initialconditions, T) data = runonesim!(model)

source

Firms actions

BeforeIT.firms_deposits — Method
firms_deposits(firms, model)

Calculate the new deposits of firms.

Arguments

  • firms: Firms object
  • model: Model object

Returns

  • DD_i: Vector of new deposits

The new deposits DD_i are calculated as follows:

DD_i = sales + labour_cost + material_cost + taxes_products + taxes_production + corporate_tax + dividend_payments + interest_payments + interest_received + investment_cost + new_credit + debt_installment

where:

  • sales = P_i * Q_i
  • labour_cost = (1 + tau_SIF) * w_i * N_i * P_bar_HH
  • material_cost = -DM_i * P_bar_i
  • taxes_products = -tau_Y_i * P_i * Y_i
  • taxes_production = -tau_K_i * P_i * Y_i
  • corporate_tax = -tau_FIRM * pos(Pi_i)
  • dividend_payments = -theta_DIV * (1 - tau_FIRM) * pos(Pi_i)
  • interest_payments = -r * (L_i + pos(-D_i))
  • interest_received = r_bar * pos(D_i)
  • investment_cost = -P_CF_i * I_i
  • new_credit = DL_i
  • debt_installment = -theta * L_i
source
BeforeIT.firms_equity — Method
firms_equity(firms, model)

Calculate the equity of firms.

Arguments

  • firms: Firms object
  • model: Model object

Returns

  • E_i: Vector of equity

The equity E_i is calculated as follows:

\[E_i = D_i + M_i * \sum(a_{sg}[:, G_i] * \bar{P}_g) + P_i * S_i + \bar{P}_{CF} * K_i - L_i\]

where:

  • D_i: Deposits
  • M_i: Intermediate goods
  • a_sg: Technology coefficient of the gth product in the sth industry
  • G_i: Vector of goods
  • P_bar_g: Producer price index for principal good g
  • P_i: Price
  • S_i: Stock
  • P_bar_CF: Capital price index
  • K_i: Capital stock
  • L_i: Loans
source
BeforeIT.firms_expectations_and_decisions — Method
firms_expectations_and_decisions(firms, model)

Calculate the expectations and decisions of firms. That is: compute firm quantity, price, investment and intermediate-goods, employment decisions, expected profits, and desired/expected loans and capital.

Arguments

  • firms: Firms object
  • model: Model object

Returns

  • Q_s_i: Vector of desired quantities
  • I_d_i: Vector of desired investments
  • DM_d_i: Vector of desired intermediate goods
  • N_d_i: Vector of desired employment
  • Pi_e_i: Vector of expected profits
  • DL_d_i: Vector of desired new loans
  • K_e_i: Vector of expected capital
  • L_e_i: Vector of expected loans
  • P_i: Vector of prices
source
BeforeIT.firms_loans — Method
firms_loans(firms, model)

Calculate the new loans of firms.

Arguments

  • firms: Firms object
  • model: Model object

Returns

  • L_i: Vector of new loans

The new loans L_i are calculated as follows:

\[L_i = (1 - theta) * L_i + DL_i\]

where:

  • theta: Rate of repayment
  • L_i: Loans
  • DL_i: Acquired new loans
source
BeforeIT.firms_production — Method
firms_production(firms)

Calculate the production of firms.

Arguments

  • firms: Firms object

Returns

  • Y_i: Vector of production

The production Y_i is computed using a Leontief technology.

source
BeforeIT.firms_profits — Method
firms_profits(firms, model)

Calculate the profits of firms.

Arguments

  • firms: Firms object
  • model: Model object

Returns

  • Pi_i: Vector of profits

The profits Pi_i are calculated as follows:

Pi_i = in_sales + in_deposits - out_wages - out_expenses - out_depreciation - out_taxes_prods - out_taxes_capital - out_loans

where:

  • in_sales = P_i * Q_i + P_i * DS_i
  • in_deposits = r_bar * pos(D_i)
  • out_wages = (1 + tau_SIF) * w_i * N_i * P_bar_HH
  • out_expenses = 1 / beta_i * P_bar_i * Y_i
  • out_depreciation = delta_i / kappa_i * P_CF_i * Y_i
  • out_taxes_prods = tau_Y_i * P_i * Y_i
  • out_taxes_capital = tau_K_i * P_i * Y_i
  • out_loans = r * (L_i + pos(-D_i))
source
BeforeIT.firms_stocks — Method
firms_stocks(firms)

Calculate the stocks of firms.

Arguments

  • firms: Firms object

Returns

  • K_i: Vector of capital stock
  • M_i: Vector of intermediate goods
  • DS_i: Vector of differneces in stock of final goods
  • S_i: Vector of stock of final goods

The stocks are calculated as follows:

K_i = K_i - delta_i / kappa_i * Y_i + I_i
+API · BeforeIT.jl
GitHub
Code reference
In this page we document the functions which constitute the bulk of BeforeIT.jl functionality.
Agent types
BeforeIT.Aggregates — Type
This is a Aggregates type. It is used to store the aggregate variables of the economy. Note that t is an integer, while the rest are floats or vectors of floats.
Fields
  • Y [vector]: GDP data + predictions
  • pi_ [vector]: inflation data + predictions
  • P_bar: Global price index
  • P_bar_g [vector]: Producer price index for principal good g
  • P_bar_HH: Consumer price index
  • P_bar_CF: Capital price index
  • P_bar_h: CPI_h
  • P_bar_CF_h: Capital price index _h
  • Y_e: Expected GDP
  • gamma_e: Expected growth
  • pi_e: Expected inflation
  • t: Time index
source
BeforeIT.Bank — Type
This is a Bank type. It represents the bank of the model.
Fields
  • E_k: equity capital (common equity) of the bank
  • Pi_k: Profits of the bank
  • Pi_e_k: Expected profits of the bank
  • D_k: Residual and balancing item on the bank’s balance sheet
  • r: Rate for loans and morgages
Household fields (bank' owner)
  • Y_h: Net disposable income of bank owner (investor)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
  • K_h: Capital stock
  • D_h: Deposits
source
BeforeIT.CentralBank — Type
This is a CentralBank type. It represents the central bank of the model.
Fields
  • r_bar: Nominal interest rate
  • r_G: Interest rate on government bonds
  • rho: Parameter for gradual adjustment of the policy rate
  • r_star: Real equilibrium interest rate
  • pi_star: Inflation target by CB
  • xi_pi: Weight the CB puts on inflation targeting
  • xi_gamma: Weight placed on economic
  • E_CB: Central bank equity
source
BeforeIT.Firms — Type
This is a Firms type. Each field is an array which stores the values for all the firms in the economy. Note that the G_i, N_i and V_i fields are integers, while the rest are floats.
For all fields the entry at index i corresponds to the ith firm.
Fields
  • G_i: Principal product
  • alpha_bar_i: Average productivity of labor
  • beta_i: Productivity of intermediate consumption
  • kappa_i: Productivity of capital
  • w_i: Wages
  • w_bar_i: Average wage rate
  • delta_i: Depreciation rate for capital
  • tau_Y_i: Net tax rate on products
  • tau_K_i: Net tax rate on production
  • N_i: Number of persons employed
  • Y_i: Production of goods
  • Q_i: Sales of goods
  • Q_d_i: Demand for goods
  • P_i: Price
  • S_i: Inventories
  • K_i: Capital, in real terms
  • M_i: Intermediate goods/services and raw materials, in real terms
  • L_i: Outstanding loans
  • pi_bar_i: Operating margin
  • D_i: Deposits of the firm
  • Pi_i: Profits
  • V_i: Vacancies
  • I_i: Investments
  • E_i: Equity
  • P_bar_i: Price index
  • P_CF_i: Price index
  • DS_i: Differnece in stock of final goods
  • DM_i: Difference in stock of intermediate goods
  • DL_i: Obtained loans
  • DL_d_i: Target loans
  • K_e_i: Expected capital 
  • L_e_i: Expected loans
  • Q_s_i: Expected sales
  • I_d_i: Desired investments
  • DM_d_i: Desired materials
  • N_d_i: Desired employment
  • Pi_e_i: Expected profits
Household fields (firms' owners)
  • Y_h: Net disposable income of firm owner (investor)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
  • K_h: Capital stock
  • D_h: Deposits of the owner of the firms
source
BeforeIT.Government — Type
This is a Government type. It represents the government of the model.
Fields
  • alpha_G: Autoregressive coefficient for government consumption
  • beta_G: Scalar constant for government consumption
  • sigma_G: Variance coefficient for government consumption
  • Y_G: Government revenues
  • C_G: Consumption demand of the general government
  • L_G: Loans taken out by the government
  • sb_inact: Social benefits for inactive persons
  • sb_other: Social benefits for all
  • C_d_j [vector]: Local governments consumption demand
  • C_j: Realised government consumption
  • P_j: Price inflation of government goods <- ??
source
BeforeIT.Model — Type
This is a Model type. It is used to store all the agents of the economy.
Fields
  • w_act: Workers that are active
  • w_inact: Workers that are inactive
  • firms: Firms
  • bank: Bank
  • cb: CentralBank
  • gov: Government
  • rotw: RestOfTheWorld
  • agg: Aggregates
source
BeforeIT.RestOfTheWorld — Type
This is a RestOfTheWorld type. It represents the rest of the world of the model.
Fields
  • alpha_E: Autoregressive coefficient for exports
  • beta_E: Scalar constant for exports
  • sigma_E: Variance coefficient for exports
  • alpha_I: Autoregressive coefficient for imports
  • beta_I: Scalar constant for imports
  • sigma_I: Variance coefficient for imports
  • Y_EA: GDP euro area
  • gamma_EA: Growth euro area
  • pi_EA: Inflation euro area
  • alpha_pi_EA: Autoregressive coefficient for euro area inflation
  • beta_pi_EA: Autoregressive coefficient for euro area inflation Scalar constant for euro area inflation
  • sigma_pi_EA: Variance coefficient for euro area inflation
  • alpha_Y_EA: Autoregressive coefficient for euro area GDP
  • beta_Y_EA: Autoregressive coefficient for euro area GDP Scalar constant for euro area GDP
  • sigma_Y_EA: Variance coefficient for euro area GDP
  • D_RoW: Net creditor/debtor position of the national economy to the rest of the world
  • Y_I: Supply of imports (in real terms)
  • C_E: Total demand for exports
  • C_d_l [vector]: Demand for exports of specific product
  • C_l: Realised consumption by foreign consumers
  • Y_m [vector]: Supply of imports per sector
  • Q_m [vector]: Sales for imports per sector
  • Q_d_m [vector]: Demand for goods
  • P_m [vector]: Price of imports per sector
  • P_l: Price inflation of exports <- ??
source
BeforeIT.Workers — Type
This is a Workers. Each field is an array which stores the values for all the workers in the economy. Note that the O_h field is an integer, while the rest are floats.
For all fields the entry at index i corresponds to the ith worker.
Fields
  • Y_h: Net disposable income of worker owner (investor)
  • D_h: Deposits
  • K_h: Capital stock
  • w_h: Wages (0 if inactive or unemployed)
  • O_h: Occupation (0 if unemployed, -1 if inactive)
  • C_d_h: Consumption budget
  • I_d_h: Investment budget
  • C_h: Realised consumption
  • I_h: Realised investment
source
Initialisation function
BeforeIT.init_model — Function
init_model(parameters, initial_conditions, T, typeInt = Int64, typeFloat = Float64)
Initializes the model with given parameters and initial conditions.
Parameters:
  • parameters: A dictionary containing the model parameters.
  • initial_conditions: A dictionary containing the initial conditions.
  • T (integer): The time horizon of the model.
  • typeInt: (optional, default: Int64): The data type to be used for integer values.
  • typeFloat: (optional, default: Float64): The data type to be used for floating-point values.
Returns:
  • model::Model: The initialized model.
source
BeforeIT.update_variables_with_totals! — Method
update_variables_with_totals!(model::Model)
Update the variables in the given model with some global quantities obtained from all agents. This is the last step in the initialization process and it must be performed after all agents have been initialized.
Arguments
  • model::Model: The model object to update.
Returns
  • Nothing
source
Functions to run an entire simulation
BeforeIT.run_one_epoch! — Method
run_one_epoch!(model; multi_threading = false)
This function simulates a single epoch the economic model, updating various components of the model based  the interactions between different economic agents. It accepts a model object, which encapsulates the state for the simulation, and an optional boolean parameter multi_threading to enable or disable multi-threading.
Key operations performed include:
  • Financial adjustments for firms and banks, including insolvency checks and profit calculations.
  • Economic expectations and adjustments, such as growth, inflation, and central bank rates.
  • Labor and credit market operations, including wage updates and loan processing.
  • Household economic activities, including consumption and investment budgeting.
  • Government and international trade financial activities, including budgeting and trade balances.
  • General market matching and accounting updates to reflect changes in economic indicators and positions.
The function updates the model in-place and does not return any value.
source
BeforeIT.run_n_sims — Method
run_n_sims(model, n_sims; shock = NoShock(), multi_threading = true)
A function that runs n_sims simulations in parallel with multiple threading and returns a vector of  data objects of dimension n_sims.
Arguments
  • model: The model configuration used to simulate.
  • n_sims: The number of simulations to run in parallel.
Returns
  • data_vector: A vector containing the data objects collected during each simulation.
source
BeforeIT.run_one_sim! — Method
run_one_sim!(model; shock = NoShock())
Run a single simulation based on the provided model.  The simulation runs for a number of epochs specified by model.prop.T.
Arguments
  • model::Model: The model configuration used for the simulation.
Returns
  • data::Data: The data collected during the simulation.
Details
The function initializes the data using BeforeIT.init_data(model), then iteratively updates the model and data for each epoch using BeforeIT.run_one_epoch!(model) and BeforeIT.update_data!(data, model) respectively.
Example
```julia model = BeforeIT.initializemodel(parameters, initialconditions, T) data = runonesim!(model)
source
Firms actions
BeforeIT.firms_deposits — Method
firms_deposits(firms, model)
Calculate the new deposits of firms.
Arguments
  • firms: Firms object
  • model: Model object
Returns
  • DD_i: Vector of new deposits
The new deposits DD_i are calculated as follows:
DD_i = sales + labour_cost + material_cost + taxes_products + taxes_production + corporate_tax + dividend_payments + interest_payments + interest_received + investment_cost + new_credit + debt_installment
where:
  • sales = P_i * Q_i
  • labour_cost = (1 + tau_SIF) * w_i * N_i * P_bar_HH
  • material_cost = -DM_i * P_bar_i
  • taxes_products = -tau_Y_i * P_i * Y_i
  • taxes_production = -tau_K_i * P_i * Y_i
  • corporate_tax = -tau_FIRM * pos(Pi_i)
  • dividend_payments = -theta_DIV * (1 - tau_FIRM) * pos(Pi_i)
  • interest_payments = -r * (L_i + pos(-D_i))
  • interest_received = r_bar * pos(D_i)
  • investment_cost = -P_CF_i * I_i
  • new_credit = DL_i
  • debt_installment = -theta * L_i
source
BeforeIT.firms_equity — Method
firms_equity(firms, model)
Calculate the equity of firms.
Arguments
  • firms: Firms object
  • model: Model object
Returns
  • E_i: Vector of equity
The equity E_i is calculated as follows:
\[E_i = D_i + M_i * \sum(a_{sg}[:, G_i] * \bar{P}_g) + P_i * S_i + \bar{P}_{CF} * K_i - L_i\]
where:
  • D_i: Deposits
  • M_i: Intermediate goods
  • a_sg: Technology coefficient of the gth product in the sth industry
  • G_i: Vector of goods
  • P_bar_g: Producer price index for principal good g
  • P_i: Price
  • S_i: Stock
  • P_bar_CF: Capital price index
  • K_i: Capital stock
  • L_i: Loans
source
BeforeIT.firms_expectations_and_decisions — Method
firms_expectations_and_decisions(firms, model)
Calculate the expectations and decisions of firms. That is: compute firm quantity, price, investment and intermediate-goods,  employment decisions, expected profits, and desired/expected loans and capital.
Arguments
  • firms: Firms object
  • model: Model object
Returns
  • Q_s_i: Vector of desired quantities
  • I_d_i: Vector of desired investments
  • DM_d_i: Vector of desired intermediate goods
  • N_d_i: Vector of desired employment
  • Pi_e_i: Vector of expected profits
  • DL_d_i: Vector of desired new loans
  • K_e_i: Vector of expected capital
  • L_e_i: Vector of expected loans
  • P_i: Vector of  prices
source
BeforeIT.firms_loans — Method
firms_loans(firms, model)
Calculate the new loans of firms.
Arguments
  • firms: Firms object
  • model: Model object
Returns
  • L_i: Vector of new loans
The new loans L_i are calculated as follows:
\[L_i = (1 - theta) * L_i + DL_i\]
where:
  • theta: Rate of repayment
  • L_i: Loans
  • DL_i: Acquired new loans
source
BeforeIT.firms_production — Method
firms_production(firms)
Calculate the production of firms.
Arguments
  • firms: Firms object
Returns
  • Y_i: Vector of production
The production Y_i is computed using a Leontief technology.
source
BeforeIT.firms_profits — Method
firms_profits(firms, model)
Calculate the profits of firms.
Arguments
  • firms: Firms object
  • model: Model object
Returns
  • Pi_i: Vector of profits
The profits Pi_i are calculated as follows:
Pi_i = in_sales + in_deposits - out_wages - out_expenses - out_depreciation - out_taxes_prods - out_taxes_capital - out_loans
where:
  • in_sales = P_i * Q_i + P_i * DS_i
  • in_deposits = r_bar * pos(D_i)
  • out_wages = (1 + tau_SIF) * w_i * N_i * P_bar_HH
  • out_expenses = 1 / beta_i * P_bar_i * Y_i
  • out_depreciation = delta_i / kappa_i * P_CF_i * Y_i
  • out_taxes_prods = tau_Y_i * P_i * Y_i
  • out_taxes_capital = tau_K_i * P_i * Y_i
  • out_loans = r * (L_i + pos(-D_i))
source
BeforeIT.firms_stocks — Method
firms_stocks(firms)
Calculate the stocks of firms.
Arguments
  • firms: Firms object
Returns
  • K_i: Vector of capital stock
  • M_i: Vector of intermediate goods
  • DS_i: Vector of differneces in stock of final goods
  • S_i: Vector of stock of final goods
The stocks are calculated as follows:
K_i = K_i - delta_i / kappa_i * Y_i + I_i
 M_i = M_i - Y_i / beta_i + DM_i
 DS_i = Y_i - Q_i
-S_i = S_i + DS_i
source
BeforeIT.firms_wages — Method
firms_wages(firms)
Calculate the wages set by firms.
Arguments
  • firms: Firms object
Returns
  • w_i: Vector of wages
source
BeforeIT.init_firms — Method
init_firms(parameters, initial_conditions; typeInt = Int64, typeFloat = Float64)
Initialize firms with given parameters and initial conditions.
Arguments
  • parameters: The parameters for initializing the firms.
  • initial_conditions: The initial conditions for the firms.
  • typeInt: (optional) The integer type to be used. Default is Int64.
  • typeFloat: (optional) The floating-point type to be used. Default is Float64.
Returns
  • firms::Firms: The initialized firms.
  • firms_args::Tuple: The arguments used to initialize the firms.
source
BeforeIT.leontief_production — Method
leontief_production(Q_s_i, N_i, alpha_i, K_i, kappa_i, M_i, beta_i)
Calculate the production function of firms.
Arguments
  • Q_s_i: Vector of desired quantities
  • N_i: Vector of employment
  • alpha_i: Vector of labour productivity
  • K_i: Vector of capital stock
  • kappa_i: Vector of capital productivity
  • M_i: Vector of intermediate goods
  • beta_i: Vector of intermediate goods productivity
Returns
  • Y_i: Vector of production
The Leontief production function Y_i is calculated as follows:
\[Y_i = \min(Q_s_i, \min(N_i \cdot \alpha_i, \min(K_i \cdot \kappa_i, M_i \cdot \beta_i)))\]
source
Households actions
Government actions
BeforeIT.gov_expenditure — Method
gov_expenditure(gov::AbstractGovernment, model)
Computes government expenditure on consumption and transfers to households.
Arguments
  • gov: government object
  • model: model object
Returns
  • C_G: government consumption
  • C_d_j: local government consumptions
source
BeforeIT.gov_loans — Method
gov_loans(gov::AbstractGovernment, model, Y_G)
Computes government new government debt.
Arguments
  • gov::AbstractGovernment: government object
  • model: model object
Returns
  • L_G: new government debt
source
BeforeIT.gov_revenues — Method
gov_revenues(model)
Computes government revenues from taxes and social security contributions. The government collects taxes on labour income, capital income, value added, and corporate income. It also collects social security contributions from workers and firms. The government also collects taxes on consumption and capital formation. Finally, the government collects taxes on exports and imports.
Arguments
  • model: model object
Returns
  • Y_G: government revenues
source
BeforeIT.gov_social_benefits — Method
gov_social_benefits(gov::AbstractGovernment, model)
Computes social benefits paid by the government households.
Arguments
  • gov: government object
  • model: model object
Returns
  • sb_other: social benefits for other households
  • sb_inact: social benefits for inactive households
source
BeforeIT.init_government — Method
init_government(parameters, initial_conditions; typeInt = Int64, typeFloat = Float64)
Initialize the government agent.
Arguments
  • parameters: The parameters.
  • initial_conditions: The initial conditions.
  • typeInt: The integer type to be used (default: Int64).
  • typeFloat: The floating-point type to be used (default: Float64).
Returns
  • The initialized government model.
  • The arguments used to initialize the government model.
source
Bank and Central Bank actions
BeforeIT._bank_deposits — Method
_deposit_bank(waD_h, wiD_h, fD_h, bD_h, fD_i, bE_k, fL_i)
Helper function to calculate the new deposits of a bank.
Arguments
  • waD_h: Array of deposits from active workers
  • wiD_h: Array of deposits from inactive workers
  • fD_h: Array of deposits from firms
  • bD_h: Deposits from the bank owner
  • fD_i: Array of deposits from firms
  • bE_k: Bank equity
  • fL_i: Array of loans to firms
Returns
  • D_k: New deposits of the bank
The new deposits D_k are calculated as the sum of the deposits of the active workers, the inactive workers, the firms, and the bank owner itself, plus the bank's equity, minus the loans of the firms.
source
BeforeIT._bank_profits — Method
_bank_profits(L_i, D_i, D_h, D_k, r_bar, r)
Helper function to calculate the total profits of a bank.
Arguments
  • L_i: Array of loans provided by the bank
  • D_i: Array of deposits from firms
  • D_h: Array of deposits from households
  • D_k: Residual and balancing item on the bank’s balance sheet
  • r_bar: Base interest rate
  • r: Interest rate set by the bank
Returns
  • Pi_k: Total profits of the bank
The total profits Pi_k are calculated as follows:
\[\Pi_k = r \cdot \sum_i(L_i + \max(0, -D_i)) + r \cdot \sum_h(\max(0, -D_h)) + r_{bar} 
+S_i = S_i + DS_i
source
BeforeIT.firms_wages — Method
firms_wages(firms)

Calculate the wages set by firms.

Arguments

  • firms: Firms object

Returns

  • w_i: Vector of wages
source
BeforeIT.init_firms — Method
init_firms(parameters, initial_conditions; typeInt = Int64, typeFloat = Float64)

Initialize firms with given parameters and initial conditions.

Arguments

  • parameters: The parameters for initializing the firms.
  • initial_conditions: The initial conditions for the firms.
  • typeInt: (optional) The integer type to be used. Default is Int64.
  • typeFloat: (optional) The floating-point type to be used. Default is Float64.

Returns

  • firms::Firms: The initialized firms.
  • firms_args::Tuple: The arguments used to initialize the firms.
source
BeforeIT.leontief_production — Method
leontief_production(Q_s_i, N_i, alpha_i, K_i, kappa_i, M_i, beta_i)

Calculate the production function of firms.

Arguments

  • Q_s_i: Vector of desired quantities
  • N_i: Vector of employment
  • alpha_i: Vector of labour productivity
  • K_i: Vector of capital stock
  • kappa_i: Vector of capital productivity
  • M_i: Vector of intermediate goods
  • beta_i: Vector of intermediate goods productivity

Returns

  • Y_i: Vector of production

The Leontief production function Y_i is calculated as follows:

\[Y_i = \min(Q_s_i, \min(N_i \cdot \alpha_i, \min(K_i \cdot \kappa_i, M_i \cdot \beta_i)))\]

source

Households actions

Government actions

BeforeIT.gov_expenditure — Method
gov_expenditure(gov::AbstractGovernment, model)

Computes government expenditure on consumption and transfers to households.

Arguments

  • gov: government object
  • model: model object

Returns

  • C_G: government consumption
  • C_d_j: local government consumptions
source
BeforeIT.gov_loans — Method
gov_loans(gov::AbstractGovernment, model, Y_G)

Computes government new government debt.

Arguments

  • gov::AbstractGovernment: government object
  • model: model object

Returns

  • L_G: new government debt
source
BeforeIT.gov_revenues — Method
gov_revenues(model)

Computes government revenues from taxes and social security contributions. The government collects taxes on labour income, capital income, value added, and corporate income. It also collects social security contributions from workers and firms. The government also collects taxes on consumption and capital formation. Finally, the government collects taxes on exports and imports.

Arguments

  • model: model object

Returns

  • Y_G: government revenues
source
BeforeIT.gov_social_benefits — Method
gov_social_benefits(gov::AbstractGovernment, model)

Computes social benefits paid by the government households.

Arguments

  • gov: government object
  • model: model object

Returns

  • sb_other: social benefits for other households
  • sb_inact: social benefits for inactive households
source
BeforeIT.init_government — Method
init_government(parameters, initial_conditions; typeInt = Int64, typeFloat = Float64)

Initialize the government agent.

Arguments

  • parameters: The parameters.
  • initial_conditions: The initial conditions.
  • typeInt: The integer type to be used (default: Int64).
  • typeFloat: The floating-point type to be used (default: Float64).

Returns

  • The initialized government model.
  • The arguments used to initialize the government model.
source

Bank and Central Bank actions

BeforeIT._bank_deposits — Method
_deposit_bank(waD_h, wiD_h, fD_h, bD_h, fD_i, bE_k, fL_i)

Helper function to calculate the new deposits of a bank.

Arguments

  • waD_h: Array of deposits from active workers
  • wiD_h: Array of deposits from inactive workers
  • fD_h: Array of deposits from firms
  • bD_h: Deposits from the bank owner
  • fD_i: Array of deposits from firms
  • bE_k: Bank equity
  • fL_i: Array of loans to firms

Returns

  • D_k: New deposits of the bank

The new deposits D_k are calculated as the sum of the deposits of the active workers, the inactive workers, the firms, and the bank owner itself, plus the bank's equity, minus the loans of the firms.

source
BeforeIT._bank_profits — Method
_bank_profits(L_i, D_i, D_h, D_k, r_bar, r)

Helper function to calculate the total profits of a bank.

Arguments

  • L_i: Array of loans provided by the bank
  • D_i: Array of deposits from firms
  • D_h: Array of deposits from households
  • D_k: Residual and balancing item on the bank’s balance sheet
  • r_bar: Base interest rate
  • r: Interest rate set by the bank

Returns

  • Pi_k: Total profits of the bank

The total profits Pi_k are calculated as follows:

\[\Pi_k = r \cdot \sum_i(L_i + \max(0, -D_i)) + r \cdot \sum_h(\max(0, -D_h)) + r_{bar} \cdot \max(0, D_k) - r_{bar} \cdot \sum_i(\max(0, D_i)) - r_{bar} \cdot -\sum_h(\max(0, D_h)) - r_{bar} \cdot \max(0, -D_k)\]

source
BeforeIT._central_bank_profits — Method
_central_bank_profits(r_bar, D_k, L_G, r_G)

Helper function to calculate the profits of a central bank.

Arguments

  • r_bar: The base interest rate
  • D_k: Deposits from commercial banks
  • L_G: Loans provided to the government
  • r_G: Interest rate on government loans

Returns

  • Pi_CB: Profits of the central bank

The profits Pi_CB are calculated as follows:

\[\{Pi}_{CB} = r_{G} \cdot L_{G} - r_{bar} \cdot D_{k}\]

source
BeforeIT.bank_deposits — Method
deposits_bank(bank, w_act, w_inact, firms)

Calculate the new deposits of a bank.

Arguments

  • bank: The Bank object containing the bank of the model
  • w_act: The Workers object containing the active workers of the model
  • w_inact: The Workers object containing the inactive workers of the model
  • firms: The Firms object containing the firms of the model

Returns

  • D_k: New deposits of the bank

The new deposits D_k are calculated as the sum of the deposits of the active workers, the inactive workers, the firms, and the bank owner itself, plus the bank's equity, minus the loans of the firms.

source
BeforeIT.bank_equity — Method
bank_equity(bank, model)

Calculate the net profits of a bank.

Arguments

  • bank: The bank object.
  • model: The model object.

Returns

  • E_k: The updated equity of the bank.

The net profits DE_k are calculated as:

\[DE_k = \Pi_k - \theta_{DIV} \cdot (1 - \tau_{FIRM}) \cdot \max(0, \Pi_k) - \tau_{FIRM} \cdot \max(0, \Pi_k)\]

and the equity E_k is updated as:

\[E_k = E_k + DE_k\]

source
BeforeIT.bank_expected_profits — Method
bank_expected_profits(Pi_k, pi_e, gamma_e)

Calculate the expected profits of a bank.

Arguments

  • Pi_k: Past profits of the bank
  • pi_e: Expected inflation rate
  • gamma_e: Expected growth rate

Returns

  • E_Pi_k: Expected profits of the bank

The expected profits E_Pi_k are calculated as follows:

\[E_{\Pi_k} = \Pi_k \cdot (1 + \pi_e) \cdot (1 + \gamma_e)\]

source
BeforeIT.bank_profits — Method
bank_profits(bank, model)

Calculate the total profits of a bank.

Arguments

  • bank: The bank object.
  • model: The model object.

Returns

  • Pi_k: The total profits of the bank.

The total profits Pi_k are calculated as:

\[\Pi_k = r \cdot \sum_i(L_i + \max(0, -D_i)) + r \cdot \sum_h(\max(0, -D_h)) + r_{bar} +\sum_h(\max(0, D_h)) - r_{bar} \cdot \max(0, -D_k)\]

source
BeforeIT._central_bank_profits — Method
_central_bank_profits(r_bar, D_k, L_G, r_G)

Helper function to calculate the profits of a central bank.

Arguments

  • r_bar: The base interest rate
  • D_k: Deposits from commercial banks
  • L_G: Loans provided to the government
  • r_G: Interest rate on government loans

Returns

  • Pi_CB: Profits of the central bank

The profits Pi_CB are calculated as follows:

\[\{Pi}_{CB} = r_{G} \cdot L_{G} - r_{bar} \cdot D_{k}\]

source
BeforeIT.bank_deposits — Method
deposits_bank(bank, w_act, w_inact, firms)

Calculate the new deposits of a bank.

Arguments

  • bank: The Bank object containing the bank of the model
  • w_act: The Workers object containing the active workers of the model
  • w_inact: The Workers object containing the inactive workers of the model
  • firms: The Firms object containing the firms of the model

Returns

  • D_k: New deposits of the bank

The new deposits D_k are calculated as the sum of the deposits of the active workers, the inactive workers, the firms, and the bank owner itself, plus the bank's equity, minus the loans of the firms.

source
BeforeIT.bank_equity — Method
bank_equity(bank, model)

Calculate the net profits of a bank.

Arguments

  • bank: The bank object.
  • model: The model object.

Returns

  • E_k: The updated equity of the bank.

The net profits DE_k are calculated as:

\[DE_k = \Pi_k - \theta_{DIV} \cdot (1 - \tau_{FIRM}) \cdot \max(0, \Pi_k) - \tau_{FIRM} \cdot \max(0, \Pi_k)\]

and the equity E_k is updated as:

\[E_k = E_k + DE_k\]

source
BeforeIT.bank_expected_profits — Method
bank_expected_profits(Pi_k, pi_e, gamma_e)

Calculate the expected profits of a bank.

Arguments

  • Pi_k: Past profits of the bank
  • pi_e: Expected inflation rate
  • gamma_e: Expected growth rate

Returns

  • E_Pi_k: Expected profits of the bank

The expected profits E_Pi_k are calculated as follows:

\[E_{\Pi_k} = \Pi_k \cdot (1 + \pi_e) \cdot (1 + \gamma_e)\]

source
BeforeIT.bank_profits — Method
bank_profits(bank, model)

Calculate the total profits of a bank.

Arguments

  • bank: The bank object.
  • model: The model object.

Returns

  • Pi_k: The total profits of the bank.

The total profits Pi_k are calculated as:

\[\Pi_k = r \cdot \sum_i(L_i + \max(0, -D_i)) + r \cdot \sum_h(\max(0, -D_h)) + r_{bar} \cdot \max(0, D_k) - r_{bar} \cdot \sum_i(\max(0, D_i)) - r_{bar} \cdot -\sum_h(\max(0, D_h)) - r_{bar} \cdot \max(0, -D_k)\]

source
BeforeIT.bank_rate — Method
bank_rate(bank, model)

Update the interest rate set by the bank.

Arguments

  • bank: The bank whose interest rate is to be updated
  • model: Model object

Returns

  • r: The updated interest rate

\[r = \bar{r} + \mu\]

source
BeforeIT.central_bank_equity — Method
central_bank_equity(cb, model)

Calculate the equity of the central bank.

Arguments

  • cb: The central bank
  • model: The model object

Returns

  • E_CB: The equity of the central bank

The equity E_CB is calculated as follows:

\[E_{CB} = E_{CB} + \Pi_{CB}\]

where \Pi_{CB} are the profits of the central bank.

source
BeforeIT.central_bank_rate — Method
central_bank_rate(cb, model)

Update the base interest rate set by the central bank according to the Taylor rule.

Arguments

  • cb: The central bank whose base interest rate is to be updated
  • model: The model object

Returns

  • r_bar: The updated base interest rate
source
BeforeIT.finance_insolvent_firms! — Method
finance_insolvent_firms!(firms, bank, P_bar_CF, zeta_b,  insolvent)

Rifinance insolvent firms using bank equity.

Arguments

  • firms: The Firms object containing the firms of the model
  • bank: The Bank object containing the bank of the model
  • P_bar_CF: Capital price index
  • zeta_b: Parameter of loan-to-capital ratio for new firms after bankruptcy

Returns

  • This function does not return a value. It modifies the banks and firms collections in-place.
source
BeforeIT.taylor_rule — Method
taylor_rule(rho, r_bar, r_star, pi_star, xi_pi, xi_gamma, gamma_EA, pi_EA)

Calculate the interest rate according to the Taylor rule.

Arguments

  • rho: Parameter for gradual adjustment of the policy rate.
  • r_bar: Nominal interest rate.
  • r_star: Real equilibrium interest rate.
  • pi_star: The target inflation rate.
  • xi_pi: Weight the CB puts on inflation targeting.
  • xi_gamma: Weight placed on economic growth.
  • gamma_EA: The output growth rate.
  • pi_EA: The inflation rate.

Returns

  • rate: The calculated interest rate.

The Taylor rule is given by the following equation:

\[r_t = ρ * r_{t-1} + (1 - ρ) * (r^* + π^* + ξ_π * (π_t - π^*) + ξ_γ * γ_t)```\]

source

Rest Of The World actions

BeforeIT.rotw_deposits — Method
rotw_deposits(rotw, tau_EXPORT)

Calculate the deposits of the rest of the world.

Arguments

  • rotw: The rest of the world object.
  • tau_EXPORT: The export tax.

Returns

  • D_RoW: The deposits of the rest of the world.

The deposits D_RoW are calculated as follows:

\[D_{RoW} = D_{RoW} + \left( \sum_{m} P_m \cdot Q_m \right) - (1 + \tau_{EXPORT}) \cdot C_l\]

source
BeforeIT.rotw_import_export — Method
rotw_import_export(rotw, model, pi_e, epsilon_E, epsilon_I)

Calculate the demand for exports and supply of imports of the rest of the world.

Arguments

  • rotw: The rest of the world object.
  • model: The model object.

Returns

  • C_E: Total demand for exports.
  • Y_I: Supply of imports (in real terms).
  • C_d_l: TDemand for exports of specific product.
  • Y_m: Supply of imports per sector.
  • P_m: Price of imports per sector.
source

Markets

BeforeIT.search_and_matching_credit — Method
search_and_matching_credit(firms::Firms, model)

This function calculates the credit allocation for each firm in the given firms object.

Parameters:

  • firms::Firms: The firms object.
  • model: The model object.

Returns:

  • DL_i: An array of credit allocations for each firm.
source
BeforeIT.search_and_matching_labour — Method
search_and_matching_labour(firms::Firms, model)

This function implements a labor search and matching algorithm. It takes in a Firms object and a Model object as input. The Firms object contains information about the number of desired employees (N_d_i) and the current number of employees (N_i) for each firm. The model object contains information about the current employment status (O_h) of each worker.

The function performs the following steps:

  • Calculates the vacancies (V_i) for each firm as the difference between desired and current employees.
  • Identifies employed workers and shuffles them randomly.
  • Fires workers from firms with negative vacancies to adjust the workforce.
  • Identifies unemployed workers and firms with positive vacancies.
  • Randomly matches unemployed workers to firms with vacancies until all vacancies are filled or there are no more unemployed workers.

The function returns:

  • N_i: An updated array of the number of employed workers for each firm.
  • O_h: An updated array where each element represents the firm a worker is employed with (0 if unemployed).
source
BeforeIT.search_and_matching! — Function
search_and_matching!(model, multi_threading::Bool = false)

This function performs a search and matching algorithm for firms and for retail markets. It takes in a model object and an optional boolean argument for multi-threading. The function loops over all goods and performs the firms market and retail market operations for each good. Finally, it updates the aggregate variables based on the results of markets.

Args:

  • model: The model object
  • multi_threading: A boolean indicating whether to use multi-threading for the algorithm. Default is false.

This function updates the model in-place and does not return any value.

source
+\sum_h(\max(0, D_h)) - r_{bar} \cdot \max(0, -D_k)\]

source
BeforeIT.bank_rate — Method
bank_rate(bank, model)

Update the interest rate set by the bank.

Arguments

  • bank: The bank whose interest rate is to be updated
  • model: Model object

Returns

  • r: The updated interest rate

\[r = \bar{r} + \mu\]

source
BeforeIT.central_bank_equity — Method
central_bank_equity(cb, model)

Calculate the equity of the central bank.

Arguments

  • cb: The central bank
  • model: The model object

Returns

  • E_CB: The equity of the central bank

The equity E_CB is calculated as follows:

\[E_{CB} = E_{CB} + \Pi_{CB}\]

where \Pi_{CB} are the profits of the central bank.

source
BeforeIT.central_bank_rate — Method
central_bank_rate(cb, model)

Update the base interest rate set by the central bank according to the Taylor rule.

Arguments

  • cb: The central bank whose base interest rate is to be updated
  • model: The model object

Returns

  • r_bar: The updated base interest rate
source
BeforeIT.finance_insolvent_firms! — Method
finance_insolvent_firms!(firms, bank, P_bar_CF, zeta_b,  insolvent)

Rifinance insolvent firms using bank equity.

Arguments

  • firms: The Firms object containing the firms of the model
  • bank: The Bank object containing the bank of the model
  • P_bar_CF: Capital price index
  • zeta_b: Parameter of loan-to-capital ratio for new firms after bankruptcy

Returns

  • This function does not return a value. It modifies the banks and firms collections in-place.
source
BeforeIT.taylor_rule — Method
taylor_rule(rho, r_bar, r_star, pi_star, xi_pi, xi_gamma, gamma_EA, pi_EA)

Calculate the interest rate according to the Taylor rule.

Arguments

  • rho: Parameter for gradual adjustment of the policy rate.
  • r_bar: Nominal interest rate.
  • r_star: Real equilibrium interest rate.
  • pi_star: The target inflation rate.
  • xi_pi: Weight the CB puts on inflation targeting.
  • xi_gamma: Weight placed on economic growth.
  • gamma_EA: The output growth rate.
  • pi_EA: The inflation rate.

Returns

  • rate: The calculated interest rate.

The Taylor rule is given by the following equation:

\[r_t = ρ * r_{t-1} + (1 - ρ) * (r^* + π^* + ξ_π * (π_t - π^*) + ξ_γ * γ_t)```\]

source

Rest Of The World actions

BeforeIT.rotw_deposits — Method
rotw_deposits(rotw, tau_EXPORT)

Calculate the deposits of the rest of the world.

Arguments

  • rotw: The rest of the world object.
  • tau_EXPORT: The export tax.

Returns

  • D_RoW: The deposits of the rest of the world.

The deposits D_RoW are calculated as follows:

\[D_{RoW} = D_{RoW} + \left( \sum_{m} P_m \cdot Q_m \right) - (1 + \tau_{EXPORT}) \cdot C_l\]

source
BeforeIT.rotw_import_export — Method
rotw_import_export(rotw, model, pi_e, epsilon_E, epsilon_I)

Calculate the demand for exports and supply of imports of the rest of the world.

Arguments

  • rotw: The rest of the world object.
  • model: The model object.

Returns

  • C_E: Total demand for exports.
  • Y_I: Supply of imports (in real terms).
  • C_d_l: TDemand for exports of specific product.
  • Y_m: Supply of imports per sector.
  • P_m: Price of imports per sector.
source

Markets

BeforeIT.search_and_matching_credit — Method
search_and_matching_credit(firms::Firms, model)

This function calculates the credit allocation for each firm in the given firms object.

Parameters:

  • firms::Firms: The firms object.
  • model: The model object.

Returns:

  • DL_i: An array of credit allocations for each firm.
source
BeforeIT.search_and_matching_labour — Method
search_and_matching_labour(firms::Firms, model)

This function implements a labor search and matching algorithm. It takes in a Firms object and a Model object as input. The Firms object contains information about the number of desired employees (N_d_i) and the current number of employees (N_i) for each firm. The model object contains information about the current employment status (O_h) of each worker.

The function performs the following steps:

  • Calculates the vacancies (V_i) for each firm as the difference between desired and current employees.
  • Identifies employed workers and shuffles them randomly.
  • Fires workers from firms with negative vacancies to adjust the workforce.
  • Identifies unemployed workers and firms with positive vacancies.
  • Randomly matches unemployed workers to firms with vacancies until all vacancies are filled or there are no more unemployed workers.

The function returns:

  • N_i: An updated array of the number of employed workers for each firm.
  • O_h: An updated array where each element represents the firm a worker is employed with (0 if unemployed).
source
BeforeIT.perform_firms_market! — Method

Perform the firms market exchange process

source
BeforeIT.perform_retail_market! — Method

Perform the retail market exchange process

source
BeforeIT.search_and_matching! — Function
search_and_matching!(model, multi_threading::Bool = false)

This function performs a search and matching algorithm for firms and for retail markets. It takes in a model object and an optional boolean argument for multi-threading. The function loops over all goods and performs the firms market and retail market operations for each good. Finally, it updates the aggregate variables based on the results of markets.

Args:

  • model: The model object
  • multi_threading: A boolean indicating whether to use multi-threading for the algorithm. Default is false.

This function updates the model in-place and does not return any value.

source
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We can now initialise the model, by specifying in advance the maximum number of epochs.

T = 16
-model = Bit.init_model(parameters, initial_conditions, T)
Model(Workers{Vector{Float64}, Vector{Int64}}([0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249  …  4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569], [3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487  …  22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958], [6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249  …  42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032], [0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983  …  11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522], [1, 2, 3, 4, 5, 6, 7, 7, 8, 9  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), Workers{Vector{Float64}, Vector{Int64}}([2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714  …  2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714], [13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984  …  13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984], [25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977  …  25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977, 25.74799087814977], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1  …  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), Firms{Vector{Float64}, Vector{Int64}}([1, 1, 1, 1, 1, 1, 1, 1, 1, 1  …  62, 62, 62, 62, 62, 62, 62, 62, 62, 62], [10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621  …  11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951, 11.988103388298951], [1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485, 1.6631751855300485  …  3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877, 3.4759574544453877], [0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754, 0.042702129272078754  …  0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946, 0.17805766706016946], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983  …  2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687, 2.6020133813757687], [0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721, 0.011078553057541721  …  0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782, 0.012425621091468782], [0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832, 0.009528627732528832  …  0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318, 0.010357147774366318], [-0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635, -0.26105054841223635  …  0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037, 0.013413693877270037], [1, 1, 1, 1, 1, 1, 2, 1, 3, 1  …  1, 1, 1, 14, 12, 1, 1, 3, 1, 3], [10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 21.644053318613242, 10.822026659306621, 32.46607997791986, 10.822026659306621  …  11.988103388298951, 11.988103388298951, 11.988103388298951, 167.8334474361853, 143.8572406595874, 11.988103388298951, 11.988103388298951, 35.96431016489685, 11.988103388298951, 35.96431016489685], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 10.822026659306621, 21.644053318613242, 10.822026659306621, 32.46607997791986, 10.822026659306621  …  11.988103388298951, 11.988103388298951, 11.988103388298951, 167.8334474361853, 143.8572406595874, 11.988103388298951, 11.988103388298951, 35.96431016489685, 11.988103388298951, 35.96431016489685], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0  …  1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [298.15365853658534, 298.15365853658534, 298.15365853658534, 298.15365853658534, 298.15365853658534, 298.15365853658534, 596.3073170731707, 298.15365853658534, 894.460975609756, 298.15365853658534  …  79.20833333333334, 79.20833333333334, 79.20833333333334, 1108.9166666666667, 950.5, 79.20833333333334, 79.20833333333334, 237.625, 79.20833333333334, 237.625], [7.65511425407355, 7.65511425407355, 7.65511425407355, 7.65511425407355, 7.65511425407355, 7.65511425407355, 15.3102285081471, 7.65511425407355, 22.965342762220647, 7.65511425407355  …  4.057486672347064, 4.057486672347064, 4.057486672347064, 56.804813412858906, 48.68984006816477, 4.057486672347064, 4.057486672347064, 12.172460017041193, 4.057486672347064, 12.172460017041193], [94.08074124608001, 94.08074124608001, 94.08074124608001, 94.08074124608001, 94.08074124608001, 94.08074124608001, 188.16148249216002, 94.08074124608001, 282.24222373824, 94.08074124608001  …  24.99375238071138, 24.99375238071138, 24.99375238071138, 349.91253332995933, 299.9250285685365, 24.99375238071138, 24.99375238071138, 74.98125714213413, 24.99375238071138, 74.98125714213413], [0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726, 0.36061475737617726  …  0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956, 0.35565736393550956], [12.384292053067004, 12.384292053067004, 12.384292053067004, 12.384292053067004, 12.384292053067004, 12.384292053067004, 24.768584106134007, 12.384292053067004, 37.15287615920101, 12.384292053067004  …  13.530111496595195, 13.530111496595195, 13.530111496595195, 189.42156095233273, 162.36133795914233, 13.530111496595195, 13.530111496595195, 40.59033448978558, 13.530111496595195, 40.59033448978558], [1.254845467476436, 1.254845467476436, 1.254845467476436, 1.254845467476436, 1.254845467476436, 1.254845467476436, 2.509690934952872, 1.254845467476436, 3.7645364024293086, 1.254845467476436  …  3.5771064837982873, 3.5771064837982873, 3.5771064837982873, 50.07949077317601, 42.925277805579455, 3.5771064837982873, 3.5771064837982873, 10.731319451394864, 3.5771064837982873, 10.731319451394864], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.3061857958470842, 1.3061857958470842, 1.3061857958470842, 1.3061857958470842, 1.3061857958470842, 1.3061857958470842, 2.0220856873365385, 1.3061857958470842, 2.7379855788259926, 1.3061857958470842  …  2.631055234104207, 2.631055234104207, 2.631055234104207, 29.1610565208097, 25.07951786131656, 2.631055234104207, 2.631055234104207, 6.712593893597363, 2.631055234104207, 6.712593893597363], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [11.889212387273576, 11.889212387273576, 11.889212387273576, 11.889212387273576, 11.889212387273576, 11.889212387273576, 18.405502707537227, 11.889212387273576, 24.921793027800877, 11.889212387273576  …  23.948487711601803, 23.948487711601803, 23.948487711601803, 265.43084109129614, 228.27970980211248, 23.948487711601803, 23.948487711601803, 61.09961900078557, 23.948487711601803, 61.09961900078557], [6.44766966354178, 6.44766966354178, 6.44766966354178, 6.44766966354178, 6.44766966354178, 6.44766966354178, 9.981535999529552, 6.44766966354178, 13.515402335517322, 6.44766966354178  …  12.98756660038165, 12.98756660038165, 12.98756660038165, 143.94648914714094, 123.7989626014857, 12.98756660038165, 12.98756660038165, 33.13509314603694, 12.98756660038165, 33.13509314603694]), Bank{Float64}(89460.0, 6476.292527744358, 0.0, 126431.00000000006, 0.028359903595743693, 3695.3696336675216, 0.0, 0.0, 0.0, 0.0, 33636.12938055408, 18241.296726948152), CentralBank{Float64}(0.0016459319014481277, 0.0089810924595537, 0.9259668580654086, -0.003424572940686137, 0.0049629315732038215, 0.30996974466133875, 1.328593153520194, 106179.90000000002), Government{Float64}(0.9905949533296431, 0.09373211872949586, 0.011235005057648862, 0.0, 14732.121510837034, 232610.9, 2.238468336136841, 0.5902859043576301, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0, 0.0), RestOfTheWorld{Float64}(0.962809216044625, 0.39260026877953946, 0.020320381298662014, 0.9662360466537488, 0.35492769963078624, 0.02122821278168188, 2.3548476e6, 0.0, 0.0019383188997990075, 0.38456173629534834, 0.0026219533879005877, 0.0025327891562467505, 0.9635784504324201, 0.5360029623199525, 0.006618207536795881, 0.0, 33097.63671130043, 34095.03119997918, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0), Aggregates{Float64, Int64}([104531.39273728609, 105062.38754395355, 105399.12953350678, 106689.88106040593, 107938.33111423723, 108890.48532381697, 110110.17779727321, 110374.00540561741, 110808.89423399912, 111932.48072916963  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.007497362866886709, -0.007895434153021436, -0.0019938777296781562, -0.0035311300388783107, 0.001212170001002849, -0.001672412335241874, 0.001839696090252002, 0.004290005139261838, 0.00600429551344886, 0.0036060572293247772  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1.0, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0  …  1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1), MutableNamedTuple(tau_VAT = 0.1528683933530887, tau_EXPORT = 0.0029486201783457183, tau_SIW = 0.17114894621657745, psi_H = 0.07125099957246343, tau_FIRM = 0.07701197259426128, H_inact = 4130, theta_DIV = 0.7858074440019603, I_s = [48, 2, 1, 1, 5, 2, 4, 1, 1, 1  …  14, 10, 13, 41, 20, 16, 7, 7, 2, 19], psi = 0.9096681249468772, tau_INC = 0.21340742230566648, zeta_b = 0.5, tau_CF = 0.08761417854834112, H_act = 4743, zeta = 0.03, mu = 0.026713971694295565, tau_G = 0.009147800682711324, theta_UB = 0.3585824478060919, T_prime = 54, tau_SIF = 0.21215146534992413, T = 16, zeta_LTV = 0.6, I = 624, products = MutableNamedTuple(a_sg = [0.37790282216028437 0.0 … 0.0 0.0; 0.0006712800413285777 0.8149348034258406 … 2.4029219530949635e-5 0.00019143106686926454; … ; 0.00037430822580994426 0.000357977071568566 … 0.3197327950788158 0.0011366219595362582; 0.0 5.36965607352849e-5 … 0.0 0.05594572929254256], b_CF_g = [0.0033476048872100555, 0.0, 0.0, 0.0008050086095806136, 0.0, 0.003306696048303853, 0.0030629933432974495, 0.0, 0.0, 0.0  …  0.0017883064872300512, 0.0, 0.0, 0.0, 0.0, 0.002291709084994238, 0.0, 0.0, 0.0, 0.0], b_CFH_g = [0.0006092803753845975, 0.0, 0.0, 0.004372477702289994, 0.0, 0.0, 0.06710603871527036, 0.0, 0.0, 0.0  …  0.0041711096896454745, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], c_E_g = [0.005090338238241512, 0.0005933402816643566, 1.793599921320476e-5, 0.007268146142708006, 0.05434404438533037, 0.02138421321578873, 0.023894091259534518, 0.028037089231640524, 0.005923381894006229, 0.011927802553688313  …  0.0014618202435669027, 0.0009447261124040238, 0.0001280209174610526, 0.0010477673386531637, 0.0, 0.0013030104043795392, 5.5260305268213854e-5, 0.0, 1.307076865739618e-5, 8.641230390167474e-6], b_HH_g = [0.011287997598927976, 0.0020040637862256817, 0.0003326837475323491, 0.00034402684015257527, 0.06305103828047173, 0.03068200872784047, 0.0003505790613555631, 0.0020473225369636873, 0.0, 0.0191077565617325  …  0.005591228759882936, 0.0004356874830029748, 0.014434120586233577, 0.03514505772295519, 0.024907176866291014, 0.013342154173060372, 0.009686226103767459, 0.011051272187723254, 0.0021206651420422927, 0.01721782824162338], c_G_g = [0.0, 0.0, 0.0, 0.0, 8.57086390274792e-6, 0.0, 0.0, 0.0, 2.4536198623552867e-5, 0.0  …  0.008555738848801895, 0.3324338967920097, 0.22067672180252998, 0.2370889178393625, 0.0477595425645907, 0.012942508661614227, 0.004667927760053456, 0.021757222045203074, 0.0, 0.002000708524749294], c_I_g = [0.016810689305736877, 0.004087420487057966, 0.00036885674795364003, 0.05818437780960789, 0.04895082866561155, 0.04689140072807505, 0.010101572733480902, 0.016066325760592727, 0.00037371898454196314, 0.041008926225895866  …  0.0018123421762754539, 0.000401097116716159, 0.0003479865324437806, 0.0006664256271585853, 0.0028556289501379516, 0.001307065586721844, 6.418152296577174e-5, 0.0, 3.538212163497461e-5, 0.00020458795490837936]), J = 156, G = 62, H = 8873, L = 312, theta = 0.05, C = [4.3800671000101816e-5 0.00010629745355671226 9.959785873214212e-5; 0.00010629745355671226 0.0004129178961230129 0.0003596689472264872; 9.959785873214212e-5 0.0003596689472264872 0.0004506370179043619]))

Note that the it is very simple to inspect the model by typing

fieldnames(typeof(model))
(:w_act, :w_inact, :firms, :bank, :cb, :gov, :rotw, :agg, :prop)

and to inspect the specific attributes of one agent type by typing

fieldnames(typeof(model.bank))
(:E_k, :Pi_k, :Pi_e_k, :D_k, :r, :Y_h, :C_d_h, :I_d_h, :C_h, :I_h, :K_h, :D_h)

We can now define a data tracker, which will store the time series of the model.

data = Bit.init_data(model);

We can run now the model for a number of epochs and progressively update the data tracker.

for t in 1:T
+model = Bit.init_model(parameters, initial_conditions, T)
Model(Workers{Vector{Float64}, Vector{Int64}}([0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249, 0.7661282859097249  …  4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569, 4.655745607447569], [3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487, 3.781806633594487  …  22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958, 22.981960001210958], [6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249, 6.973481059156249  …  42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032, 42.37769888790032], [0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983, 0.2697101055708983  …  11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522, 11.337587012313522], [1, 2, 2, 2, 2, 2, 2, 2, 2, 2  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), Workers{Vector{Float64}, Vector{Int64}}([2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714  …  2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714, 2.8287542404944714], [13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984  …  13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984, 13.963459838592984], [25.74799087814977, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.3061857958470848, 36.38528047883038, 1.3061857958470848, 1.3061857958470848, 3.4538854703154493, 3.4538854703154493, 1.3061857958470848, 2.7379855788259944, 1.3061857958470848, 1.3061857958470848  …  2.631055234104208, 2.631055234104208, 2.631055234104208, 2.631055234104208, 2.631055234104208, 20.9979792018234, 2.631055234104208, 2.631055234104208, 2.631055234104208, 2.631055234104208], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [11.88921238727358, 331.1874380801928, 11.88921238727358, 11.88921238727358, 31.438083348064552, 31.438083348064552, 11.88921238727358, 24.921793027800895, 11.88921238727358, 11.88921238727358  …  23.94848771160181, 23.94848771160181, 23.94848771160181, 23.94848771160181, 23.94848771160181, 191.1285785129287, 23.94848771160181, 23.94848771160181, 23.94848771160181, 23.94848771160181], [6.447669663541784, 179.6071201269428, 6.447669663541784, 6.447669663541784, 17.049268671505107, 17.049268671505107, 6.447669663541784, 13.515402335517333, 6.447669663541784, 6.447669663541784  …  12.987566600381651, 12.987566600381651, 12.987566600381651, 12.987566600381651, 12.987566600381651, 103.6514360558304, 12.987566600381651, 12.987566600381651, 12.987566600381651, 12.987566600381651]), Bank{Float64}(89460.0, 6476.292527744357, 0.0, 126431.00000000009, 0.028359903595743693, 3695.3696336675207, 0.0, 0.0, 0.0, 0.0, 33636.12938055406, 18241.296726948145), CentralBank{Float64}(0.0016459319014481277, 0.0089810924595537, 0.9259668580654086, -0.003424572940686137, 0.0049629315732038215, 0.30996974466133875, 1.328593153520194, 106179.90000000002), Government{Float64}(0.9905949533296431, 0.09373211872949586, 0.011235005057648862, 0.0, 14732.121510837034, 232610.9, 2.238468336136841, 0.5902859043576301, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0, 0.0), RestOfTheWorld{Float64}(0.962809216044625, 0.39260026877953946, 0.020320381298662014, 0.9662360466537488, 0.35492769963078624, 0.02122821278168188, 2.3548476e6, 0.0, 0.0019383188997990075, 0.38456173629534834, 0.0026219533879005877, 0.0025327891562467505, 0.9635784504324201, 0.5360029623199525, 0.006618207536795881, 0.0, 33097.63671130043, 34095.03119997918, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0), Aggregates{Float64, Int64}([104531.39273728609, 105062.38754395355, 105399.12953350678, 106689.88106040593, 107938.33111423723, 108890.48532381697, 110110.17779727321, 110374.00540561741, 110808.89423399912, 111932.48072916963  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.007497362866886709, -0.007895434153021436, -0.0019938777296781562, -0.0035311300388783107, 0.001212170001002849, -0.001672412335241874, 0.001839696090252002, 0.004290005139261838, 0.00600429551344886, 0.0036060572293247772  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1.0, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0  …  1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1), MutableNamedTuple(tau_VAT = 0.1528683933530887, tau_EXPORT = 0.0029486201783457183, tau_SIW = 0.17114894621657745, psi_H = 0.07125099957246343, tau_FIRM = 0.07701197259426128, H_inact = 4130, theta_DIV = 0.7858074440019603, I_s = [48, 2, 1, 1, 5, 2, 4, 1, 1, 1  …  14, 10, 13, 41, 20, 16, 7, 7, 2, 19], psi = 0.9096681249468772, tau_INC = 0.21340742230566648, zeta_b = 0.5, tau_CF = 0.08761417854834112, H_act = 4743, zeta = 0.03, mu = 0.026713971694295565, tau_G = 0.009147800682711324, theta_UB = 0.3585824478060919, T_prime = 54, tau_SIF = 0.21215146534992413, T = 16, zeta_LTV = 0.6, I = 624, products = MutableNamedTuple(a_sg = [0.37790282216028437 0.0 … 0.0 0.0; 0.0006712800413285777 0.8149348034258406 … 2.4029219530949635e-5 0.00019143106686926454; … ; 0.00037430822580994426 0.000357977071568566 … 0.3197327950788158 0.0011366219595362582; 0.0 5.36965607352849e-5 … 0.0 0.05594572929254256], b_CF_g = [0.0033476048872100555, 0.0, 0.0, 0.0008050086095806136, 0.0, 0.003306696048303853, 0.0030629933432974495, 0.0, 0.0, 0.0  …  0.0017883064872300512, 0.0, 0.0, 0.0, 0.0, 0.002291709084994238, 0.0, 0.0, 0.0, 0.0], b_CFH_g = [0.0006092803753845975, 0.0, 0.0, 0.004372477702289994, 0.0, 0.0, 0.06710603871527036, 0.0, 0.0, 0.0  …  0.0041711096896454745, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], c_E_g = [0.005090338238241512, 0.0005933402816643566, 1.793599921320476e-5, 0.007268146142708006, 0.05434404438533037, 0.02138421321578873, 0.023894091259534518, 0.028037089231640524, 0.005923381894006229, 0.011927802553688313  …  0.0014618202435669027, 0.0009447261124040238, 0.0001280209174610526, 0.0010477673386531637, 0.0, 0.0013030104043795392, 5.5260305268213854e-5, 0.0, 1.307076865739618e-5, 8.641230390167474e-6], b_HH_g = [0.011287997598927976, 0.0020040637862256817, 0.0003326837475323491, 0.00034402684015257527, 0.06305103828047173, 0.03068200872784047, 0.0003505790613555631, 0.0020473225369636873, 0.0, 0.0191077565617325  …  0.005591228759882936, 0.0004356874830029748, 0.014434120586233577, 0.03514505772295519, 0.024907176866291014, 0.013342154173060372, 0.009686226103767459, 0.011051272187723254, 0.0021206651420422927, 0.01721782824162338], c_G_g = [0.0, 0.0, 0.0, 0.0, 8.57086390274792e-6, 0.0, 0.0, 0.0, 2.4536198623552867e-5, 0.0  …  0.008555738848801895, 0.3324338967920097, 0.22067672180252998, 0.2370889178393625, 0.0477595425645907, 0.012942508661614227, 0.004667927760053456, 0.021757222045203074, 0.0, 0.002000708524749294], c_I_g = [0.016810689305736877, 0.004087420487057966, 0.00036885674795364003, 0.05818437780960789, 0.04895082866561155, 0.04689140072807505, 0.010101572733480902, 0.016066325760592727, 0.00037371898454196314, 0.041008926225895866  …  0.0018123421762754539, 0.000401097116716159, 0.0003479865324437806, 0.0006664256271585853, 0.0028556289501379516, 0.001307065586721844, 6.418152296577174e-5, 0.0, 3.538212163497461e-5, 0.00020458795490837936]), J = 156, G = 62, H = 8873, L = 312, theta = 0.05, C = [4.3800671000101816e-5 0.00010629745355671226 9.959785873214212e-5; 0.00010629745355671226 0.0004129178961230129 0.0003596689472264872; 9.959785873214212e-5 0.0003596689472264872 0.0004506370179043619]))

Note that the it is very simple to inspect the model by typing

fieldnames(typeof(model))
(:w_act, :w_inact, :firms, :bank, :cb, :gov, :rotw, :agg, :prop)

and to inspect the specific attributes of one agent type by typing

fieldnames(typeof(model.bank))
(:E_k, :Pi_k, :Pi_e_k, :D_k, :r, :Y_h, :C_d_h, :I_d_h, :C_h, :I_h, :K_h, :D_h)

We can now define a data tracker, which will store the time series of the model.

data = Bit.init_data(model);

We can run now the model for a number of epochs and progressively update the data tracker.

for t in 1:T
     Bit.run_one_epoch!(model; multi_threading = true)
     Bit.update_data!(data, model)
-end

Note that we can equivalently run the model for a number of epochs in the single command data = BeforeIT.run_one_sim!(model), but writing the loop explicitely is more instructive.

We can then plot any time series stored in the data tracker, for example

plot(data.real_gdp, title = "gdp", titlefont = 10)
Example block output

Or we can plot multiple time series at once using the function plot_data

ps = Bit.plot_data(data, quantities = [:real_gdp, :real_household_consumption, :real_government_consumption, :real_capitalformation, :real_exports, :real_imports, :wages, :euribor, :gdp_deflator])
-plot(ps..., layout = (3, 3))
Example block output

To run multiple monte-carlo repetitions in parallel we can use

model = Bit.init_model(parameters, initial_conditions, T)
-data_vector = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72421.99999999999, 71654.7455193535, 72708.19120661693, 72262.58743704572, 70798.69074115132, 70962.31665368262, 71742.2066892932, 71112.12078774499, 71733.54163828376, 72076.64431680735, 72308.99664352532, 73143.8830930201, 74244.43752728937, 74673.05864813716, 74967.85688726339, 75143.01412648667, 74721.51437337777], [72421.99999999999, 71652.1187371939, 72492.2242182431, 72337.49254536178, 70962.60956028242, 70884.78246023403, 71709.14414396447, 70809.05698838369, 70676.3861435541, 71047.2304648144, 70975.64908845791, 71540.42372005866, 72281.0966099183, 72526.49699640412, 72598.19219944687, 72520.3925837881, 72417.89667213589], [64900.92049553802, 64212.85525279031, 65161.6852009054, 64766.55048357167, 63450.22322181534, 63629.48885251573, 64331.97433547539, 63755.20028254892, 64334.8156568142, 64627.817316240034, 64836.410285503196, 65576.16462002849, 66564.13249277808, 66947.32465188768, 67210.9640663313, 67372.71627644837, 66989.13909216631], [64900.92049553802, 64210.501281935045, 64968.13379113078, 64833.6854375125, 63597.12827832479, 63559.96658590482, 64302.326811155915, 63483.4900169482, 63386.697073502495, 63704.789184172936, 63640.854087154745, 64138.60468532931, 64803.89173528908, 65022.8479746832, 65086.48759341311, 65021.29160775848, 64923.90569991257], [40512.94792630534, 40076.64102433958, 40638.059128314904, 40379.70318278459, 39592.91975777675, 39439.863574202005, 39851.86684807819, 39548.141217656994, 39729.40208741553, 40033.262332840335, 40134.81273835296, 40645.675969963675, 41268.230048609556, 41530.90903910021, 41695.323727546376, 41777.071761690335, 41610.57597524104], [40512.94792630534, 40075.17185987745, 40517.35086838369, 40421.55950357172, 39684.588467815134, 39396.77115337709, 39833.50103226487, 39379.5959710474, 39143.89975810327, 39461.49882780319, 39394.743633237485, 39754.64192380146, 40176.92129417074, 40337.05601742315, 40377.37306993556, 40318.97948702269, 40327.747861018775], [14866.888022051655, 14705.783087554224, 14805.022041111792, 14601.273837111406, 14547.811801429812, 14549.368153040326, 14644.501687006443, 14610.139853780045, 14719.171040655963, 14804.30428787132, 15019.016215571297, 15047.366308738065, 15165.877726572582, 15320.597895613295, 15361.060914223339, 15313.190779588354, 15272.663138304222], [14866.888022051655, 14705.243990131195, 14761.046307842049, 14616.409054892993, 14581.494064568704, 14533.471356287768, 14637.752737912375, 14547.874737169288, 14502.250863732896, 14592.86608833378, 14742.071858989035, 14717.498115721231, 14764.826963982017, 14880.189955725504, 14875.511971953694, 14778.733857772591, 14801.81645585213], [15944.236265986641, 15835.04049950136, 16064.751443457819, 16006.003585925015, 15347.819713907877, 15553.993393597078, 15885.65049672127, 15565.367149895885, 15743.530990107638, 15721.89994326207, 15974.3837981781, 16144.702476180657, 16374.807054776295, 16626.78634727785, 16196.511019264317, 16596.20399799826, 16512.105482957446], [15944.236265986641, 15834.460004775157, 16017.033903925601, 16022.594902051827, 15383.35421966504, 15536.998932458435, 15878.329561614024, 15499.031070270565, 15511.51455940411, 15497.356448838207, 15679.822864273654, 15790.778492186219, 15941.787022900082, 16148.830540849716, 15684.554271108951, 16016.967689232359, 16003.047565779241], [15944.236265986641, 15766.51450822148, 15994.967302258689, 15903.7583076511, 15586.438724859345, 15555.147203374338, 15709.607877318569, 15624.605563134128, 15711.979183236477, 15788.192831660635, 15817.906984717847, 16002.326450651793, 16288.921698057973, 16399.92715937046, 16462.840227747725, 16503.299522940044, 16416.78982443486], [15944.236265986641, 15765.936525580833, 15947.457044334786, 15920.243639562352, 15622.52570052927, 15538.151481570672, 15702.368071819015, 15558.01702276955, 15480.427739556322, 15562.702528207954, 15526.231417591212, 15651.523632269098, 15858.172842860633, 15928.492677258237, 15942.465059393602, 15927.305741513952, 15910.670429582115], [3173.2320350842083, 3130.9056470483874, 3182.3302966771303, 3160.5789168298384, 3099.5349936922676, 3043.627663612538, 3063.2048222420626, 3088.552811310636, 3072.7391227884473, 3106.7946958283223, 3099.615545295257, 3133.315719784448, 3222.4575594088356, 3253.0748769478496, 3268.490777812241, 3266.080642970377, 3252.916320582834], [3173.2320350842083, 3130.7908715783533, 3172.877740111242, 3163.855072784157, 3106.7112862296426, 3040.302163161249, 3061.793137921771, 3075.390096724784, 3027.455382806583, 3062.422798031212, 3042.4599353323206, 3064.6272082250284, 3137.241979742631, 3159.561554907883, 3165.1768043278094, 3152.088762899845, 3152.630938526621], [34195.564496956766, 34574.59220175692, 34970.94664047678, 34869.13904117746, 34330.87423991417, 34500.88217577725, 34610.860909138646, 35923.46637490656, 36967.8082770612, 36782.83639097689, 36188.47613403506, 37285.10902847962, 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Note that this will use the number of threads specified when activating the Julia environment. To discover the number of threads available, you can use the command

Threads.nthreads()
2

To activate Julia with a specific number of threads, say 8, you can use the command julia -t 8 in the terminal.

We can then plot the results of the monte-carlo repetitions using the function plot_data_vector

ps = Bit.plot_data_vector(data_vector)
-plot(ps..., layout = (3, 3))
Example block output +end

Note that we can equivalently run the model for a number of epochs in the single command data = BeforeIT.run_one_sim!(model), but writing the loop explicitely is more instructive.

We can then plot any time series stored in the data tracker, for example

plot(data.real_gdp, title = "gdp", titlefont = 10)
Example block output

Or we can plot multiple time series at once using the function plot_data

ps = Bit.plot_data(data, quantities = [:real_gdp, :real_household_consumption, :real_government_consumption, :real_capitalformation, :real_exports, :real_imports, :wages, :euribor, :gdp_deflator])
+plot(ps..., layout = (3, 3))
Example block output

To run multiple monte-carlo repetitions in parallel we can use

model = Bit.init_model(parameters, initial_conditions, T)
+data_vector = Bit.run_n_sims(model, 4)
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3105.124088232201, 3137.018797692113, 3179.859041231613, 3160.355627655964, 3172.902801974512, 3167.3327694483783, 3197.2590879232202, 3210.1840715592575, 3217.6533169093073, 3203.4887847435352, 3200.855012672573, 3207.1139247160236, 3199.0061582059843, 3208.3918019221205], [34195.564496956766, 35062.037731727825, 35982.95052721593, 36296.00991056556, 35546.54107366324, 36276.82549816845, 36859.39834385315, 37488.71781404889, 36313.4053790304, 37473.24931142507, 37024.725989644234, 37744.687129744256, 38471.009587584354, 39432.68190400875, 39646.3766671181, 40070.39018761919, 41193.678444311234], [34195.564496956766, 34969.08455519565, 35581.16702842936, 35641.54560124759, 34821.319476335724, 35544.37642786833, 36002.200251952905, 36483.13798978159, 35316.77685313023, 36266.70808029319, 35548.055694414885, 36251.59576039371, 36797.527450092304, 37672.669614113314, 37821.45308828083, 38065.035504838095, 39103.73008390194], [33097.63671130043, 33893.73601670172, 34694.78475442241, 35059.93847899736, 34472.51748731154, 34954.50585570227, 35537.96002341086, 36123.18458302415, 34785.61701862961, 35812.85535147059, 35518.148798847156, 36269.08331243733, 36560.954937367525, 37569.035847458246, 37382.43491931944, 37760.25922957543, 38890.48660315909], [33097.63671130043, 33803.880131787046, 34307.38483851683, 34427.76214672487, 33769.20815141186, 34248.75514666897, 34711.49315496356, 35154.233183161676, 33830.91894363649, 34659.77449032046, 34101.56585674886, 34834.363372067724, 34970.55984055984, 35892.20430523627, 35661.72062829078, 35870.51689578468, 36917.38995869044], [29576.147776884874, 29478.455117739497, 29631.564937805808, 29921.2641029869, 30285.993987764195, 30308.893948661706, 30264.51204445919, 30637.12435310864, 30724.196346283654, 31211.67896189761, 31439.366607644206, 31388.824707520052, 31426.579208531224, 31829.524479833577, 32088.28568864368, 32286.093296008563, 32630.628199641535], [34346.71094688275, 34233.26069127104, 34446.33549768362, 34838.910970989484, 35321.921576385415, 35211.05434916723, 35211.366464441715, 35641.187479789696, 35732.77815185632, 36279.12723247449, 36425.97350643747, 36416.952214849574, 36424.866304391886, 36967.8340392693, 37269.98391376492, 37455.183504920366, 37910.58390699554], [28335.32931213966, 28241.735187267688, 28417.517515222116, 28741.384197338884, 29139.858001317247, 29048.394821683854, 29048.65231035867, 29403.245797752505, 29478.806216301502, 29929.53295816165, 30050.67811052971, 30043.235730723405, 30049.76469164004, 30497.70189288796, 30746.969317902705, 30899.755167237116, 31275.451121986236], [978.0617717704338, 974.8311463349157, 984.9162877814103, 999.0579674759916, 1015.059165351727, 1008.9753587137756, 1009.7315058416891, 1025.0598765298191, 1028.252649561654, 1037.2469718366033, 1040.4020094868042, 1038.6591608431183, 1043.2953786581693, 1059.3808674366353, 1069.9582727629718, 1071.4359804863989, 1086.679275382532], [0.0019383188997990075, 0.004541817407205384, 0.00621452640844411, 0.005491279574728614, -0.00016891463956747454, 0.004469047144255223, 0.002406234700440324, 0.004008019640976412, 0.0039041332330809198, 0.005712531780104113, 0.007820684046808912, 0.0012205587729923195, 0.003934902993571354, 0.006509810248073844, 0.008177247539066457, 0.00683587632727245, 0.005495233105617059], [2.3548476e6, 2.373368690247368e6, 2.3902886727624224e6, 2.374539111765473e6, 2.3518175040765824e6, 2.3680588397727218e6, 2.360140417024101e6, 2.366116698788499e6, 2.347323571712904e6, 2.364596459839206e6, 2.3610990671290155e6, 2.3761381224565282e6, 2.376466461942939e6, 2.395374522688831e6, 2.403931161595387e6, 2.4134109280189206e6, 2.416717882889374e6], [0.0016459319014481277, 0.0024019139195420123, 0.003067921585415345, 0.0023187163344444654, 0.001201986883741552, 0.0018948162944946814, 0.0014808550585820408, 0.0017122631383910552, 0.0008938571998394019, 0.0016825595386059525, 0.0015919829352104538, 0.002128637857005705, 0.0020749379664123176, 0.0028532996886458635, 0.003181069185235309, 0.0034903113588503933, 0.0034927942529015097], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086171; 516.3721631424412 257.3902657915165 … 136.54471427633868 503.23688439713067; … ; 558.3170578884872 282.99107924022735 … 148.39324205557122 542.5213481619362; 561.0956173621873 286.75769165130953 … 149.75496266215623 547.1843596049332], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086171; 515.0032058329953 256.7078969287355 … 136.18271977311616 501.9027501032035; … ; 530.3756347760369 268.8285646432266 … 140.96678372921448 515.3704339594236; 532.6286071357463 272.209130072581 … 142.1571915841973 519.4231326792064])])

Note that this will use the number of threads specified when activating the Julia environment. To discover the number of threads available, you can use the command

Threads.nthreads()
2

To activate Julia with a specific number of threads, say 8, you can use the command julia -t 8 in the terminal.

We can then plot the results of the monte-carlo repetitions using the function plot_data_vector

ps = Bit.plot_data_vector(data_vector)
+plot(ps..., layout = (3, 3))
Example block output diff --git a/previews/PR39/examples/change_expectations-198b3f9b.svg b/previews/PR39/examples/change_expectations-eb4c8a9b.svg similarity index 86% rename from previews/PR39/examples/change_expectations-198b3f9b.svg rename to previews/PR39/examples/change_expectations-eb4c8a9b.svg index 4c1ad85..f0ca5b1 100644 --- a/previews/PR39/examples/change_expectations-198b3f9b.svg +++ b/previews/PR39/examples/change_expectations-eb4c8a9b.svg @@ -1,284 +1,284 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/previews/PR39/examples/change_expectations-a15ca6c0.svg b/previews/PR39/examples/change_expectations-fda15fab.svg similarity index 86% rename from previews/PR39/examples/change_expectations-a15ca6c0.svg rename to previews/PR39/examples/change_expectations-fda15fab.svg index 897c349..4986473 100644 --- a/previews/PR39/examples/change_expectations-a15ca6c0.svg +++ b/previews/PR39/examples/change_expectations-fda15fab.svg @@ -1,82 +1,82 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/previews/PR39/examples/change_expectations.html b/previews/PR39/examples/change_expectations.html index 8b42872..e1224fd 100644 --- a/previews/PR39/examples/change_expectations.html +++ b/previews/PR39/examples/change_expectations.html @@ -35,7 +35,7 @@ p2 = plot(data.real_household_consumption, title = "consumption", titlefont = 10) plot!(p2, data_back.real_household_consumption, titlefont = 10, label = "backward looking") -plot(p1, p2, layout = (2, 1), legend = true)Example block output

Plot all time series

p1 = plot(data.real_gdp, title = "gdp", titlefont = 10)
+plot(p1, p2, layout = (2, 1), legend = true)
Example block output

Plot all time series

p1 = plot(data.real_gdp, title = "gdp", titlefont = 10)
 plot!(p1, data_back.real_gdp, titlefont = 10)
 p2 = plot(data.real_household_consumption, title = "household cons.", titlefont = 10)
 plot!(p2, data_back.real_household_consumption, titlefont = 10)
@@ -54,4 +54,4 @@
 p9 = plot(data.nominal_gdp ./ data.real_gdp, title = "gdp deflator", titlefont = 10)
 plot!(p9, data_back.nominal_gdp ./ data_back.real_gdp, titlefont = 10)
 
-plot(p1, p2, p3, p4, p5, p6, p7, p8, p9, layout = (3, 3), legend = false)
Example block output

Note that, importantly, once the function estimate_next_value has been changed, the model will use the new expectations in all the simulations, unless the function is changed again. To restore the original expectations you could close the Julia session.

+plot(p1, p2, p3, p4, p5, p6, p7, p8, p9, layout = (3, 3), legend = false)Example block output

Note that, importantly, once the function estimate_next_value has been changed, the model will use the new expectations in all the simulations, unless the function is changed again. To restore the original expectations you could close the Julia session.

diff --git a/previews/PR39/examples/get_parameters_and_initial_conditions.html b/previews/PR39/examples/get_parameters_and_initial_conditions.html index 228fab2..acc8831 100644 --- a/previews/PR39/examples/get_parameters_and_initial_conditions.html +++ b/previews/PR39/examples/get_parameters_and_initial_conditions.html @@ -31,4 +31,4 @@ ".jld2", init_conds, ) -end +end diff --git a/previews/PR39/examples/multithreading_speedup.html b/previews/PR39/examples/multithreading_speedup.html index b3c9234..620a6bd 100644 --- a/previews/PR39/examples/multithreading_speedup.html +++ b/previews/PR39/examples/multithreading_speedup.html @@ -7,5 +7,5 @@ @time data = Bit.run_one_sim!(model; multi_threading = false); model = Bit.init_model(parameters, initial_conditions, T); -@time data = Bit.run_one_sim!(model; multi_threading = true);
  5.282855 seconds (3.52 M allocations: 6.415 GiB, 17.94% gc time)
-  2.753682 seconds (3.49 M allocations: 6.293 GiB, 14.20% gc time)

Is the speedup in line to what we would expect? Yes!

+@time data = Bit.run_one_sim!(model; multi_threading = true);
  5.102726 seconds (3.52 M allocations: 6.415 GiB, 17.49% gc time)
+  2.725590 seconds (3.38 M allocations: 6.080 GiB, 14.19% gc time)

Is the speedup in line to what we would expect? Yes!

diff --git a/previews/PR39/examples/scenario_analysis_via_overload-ae031e54.svg b/previews/PR39/examples/scenario_analysis_via_overload-ae031e54.svg deleted file mode 100644 index 64982da..0000000 --- a/previews/PR39/examples/scenario_analysis_via_overload-ae031e54.svg +++ /dev/null @@ -1,46 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/previews/PR39/examples/scenario_analysis_via_overload-c6079948.svg b/previews/PR39/examples/scenario_analysis_via_overload-c6079948.svg new file mode 100644 index 0000000..1f812bb --- /dev/null +++ b/previews/PR39/examples/scenario_analysis_via_overload-c6079948.svg @@ -0,0 +1,48 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/previews/PR39/examples/scenario_analysis_via_overload.html b/previews/PR39/examples/scenario_analysis_via_overload.html index 8a4729a..95f596d 100644 --- a/previews/PR39/examples/scenario_analysis_via_overload.html +++ b/previews/PR39/examples/scenario_analysis_via_overload.html @@ -25,7 +25,7 @@ "D_I" => 54049.0 ⋮ => ⋮

Initialise the model and the data collector

T = 20
 model = Bit.init_model(parameters, initial_conditions, T);
-data = Bit.init_data(model);

Simulate the model for T quarters

data_vec_baseline = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72421.99999999999, 72482.24164365747, 72806.70541130209, 72754.16475403759, 72549.8441087749, 72773.28962179415, 73223.35624273312, 73450.80095275612, 73486.9689255273, 73390.09900874771  …  73487.87786285818, 73552.24364147506, 73420.00630204986, 73422.19594178893, 73871.5084247658, 74034.12903406144, 74123.34782807795, 74123.23503362673, 74295.6437649165, 74607.34849704895], [72421.99999999999, 72409.21932565059, 72660.080908229, 72534.49740509632, 72257.92392555343, 72407.44982310369, 72781.85579710368, 72934.37720609625, 72896.77688242168, 72727.34181784363  …  72677.57785841805, 72667.95068003482, 72464.22539418185, 72393.38018445886, 72763.01764239161, 72849.73146200035, 72864.04194259284, 72790.52421298879, 72886.32962524737, 73118.3841425906], [64900.92049553803, 64966.37094093018, 65262.47291492102, 65202.779982977794, 64996.44480689832, 65204.179235701464, 65623.4985132529, 65824.15436383882, 65841.0946100106, 65743.39771731455  …  65827.26008189173, 65881.93582747875, 65751.92105526871, 65748.20419221747, 66169.97928418736, 66327.26946073397, 66406.1598184888, 66391.45493681531, 66553.83147428026, 66852.52389839568], [64900.92049553803, 64900.920495538005, 65131.04164624304, 65005.91260265827, 64734.91737965359, 64876.39023606522, 65227.82143255039, 65361.35265199424, 65312.308476681654, 65149.694885731995  …  65101.428411583045, 65089.86030056111, 64895.963204465355, 64826.91891027441, 65177.05503408741, 65266.16618396558, 65277.96377308284, 65197.75892028216, 65291.37177956863, 65518.32522902166], [40512.94792630534, 40450.62290390966, 40606.41829335058, 40690.79352286174, 40714.701138735836, 40726.56345252486, 40805.78521580877, 40967.30424451112, 41064.093913700446, 41079.96670283071  …  41135.098275436205, 41216.133774942165, 41234.943797579814, 41283.472501308104, 41403.97972457797, 41448.70025172508, 41548.885675829704, 41686.67569399479, 41709.34595244866, 41707.81387692488], [40512.94792630534, 40409.870877174675, 40524.64154119288, 40567.93541886596, 40550.876623849086, 40521.82627694498, 40559.74661171478, 40679.26805601386, 40734.298007847116, 40708.98964050031  …  40681.529996087644, 40720.6065797064, 40698.14771155662, 40704.995019371694, 40782.68565793464, 40785.60418260479, 40842.99801964506, 40937.163294156766, 40918.16132804284, 40875.436779813914], [14866.888022051655, 14894.41295371824, 14925.358501867855, 14830.760493123653, 15060.470881893863, 15209.735991778232, 15531.088291711927, 15624.369723051093, 15702.584762810815, 15639.584328261926  …  15792.025662206652, 15629.74180621797, 15456.374453734103, 15664.098052105235, 15527.857114064258, 15322.712868705696, 15064.046647107905, 15115.755877516434, 15227.53119104298, 15189.170536190839], [14866.888022051655, 14879.407560196021, 14895.300511176469, 14785.981835416298, 14999.871779673143, 15133.274878335582, 15437.443548371137, 15514.516658865085, 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15521.948153932519, 15310.289105974178], [14866.888022051655, 14702.714187099356, 14821.66972100056, 14863.096535651506, 14995.975804140971, 14994.07156222176, 15008.638990817992, 15007.180474874724, 15019.583719151693, 15101.793071707125  …  15561.928696472098, 15473.948648123696, 15436.415015314977, 15396.191905698399, 14943.951031698518, 15143.598404824153, 14902.584421848975, 15074.802206046228, 15227.512304130127, 15004.736433288861], [15944.236265986641, 16061.299044754092, 15886.855608037093, 16024.841688054705, 16174.646046188962, 16220.541739882037, 17858.16806499166, 17028.154707182457, 16685.698568336476, 16584.136566860045  …  18452.531652511614, 17882.74235211973, 17966.837804919123, 17992.222046653667, 17774.55377291215, 17253.900536128705, 17909.886629695542, 17126.50574763392, 17675.678051111863, 17589.35621194992], [15944.236265986641, 16045.11807049256, 15854.861270485877, 15976.457729518217, 16109.563816235934, 16138.999188272659, 17750.4921871927, 16908.431799623653, 16551.691591689003, 16434.371736002995  …  18249.068347885146, 17667.74438046971, 17732.94569003624, 17740.109162892284, 17507.834851934, 16977.872734219065, 17605.60968724747, 16818.576938007223, 17340.38809677104, 17238.319418054358], [15944.236265986641, 15943.592785113624, 15982.570760959392, 16046.973794683761, 16135.052623465852, 16240.905425286894, 17698.270399169145, 16540.58931830895, 16614.917605669227, 16663.17311254421  …  18367.34273800823, 17102.514519349475, 18629.92685142429, 17306.761988722545, 17350.748631231254, 17380.206381492542, 17436.8736398972, 17439.908434704626, 17484.04166217799, 17511.22619091729], [15944.236265986641, 15927.530394159228, 15950.383663872435, 15998.523012464997, 16070.12970631849, 16159.260503060386, 17591.55862482485, 16424.29442435378, 16481.479088458585, 16512.694533651607  …  18164.818752624713, 16896.896954695625, 18387.402649964642, 17064.253994866376, 17090.38918636363, 17102.15793939252, 17140.632870388123, 17126.344750236232, 17152.386858687383, 17161.748664556013], [3173.2320350842083, 3159.709416155229, 3145.8854344174483, 3137.7158150014293, 3123.359092521994, 3151.5168030382806, 2582.4184648025785, 3207.6289020210984, 3228.9425781266827, 3244.080674089506  …  3067.3750673236445, 3287.7704334160453, 3253.342248767016, 3324.1093791201147, 3339.760273124705, 3347.7543839372074, 3355.96574767909, 3373.705117796693, 3377.9411998163137, 3384.3571628760637], [3173.2320350842083, 3156.5261632568045, 3139.5499755343635, 3128.242079482064, 3110.7915733252885, 3135.6737611916756, 2566.847764939641, 3185.0764490323054, 3203.0101407827565, 3214.784654276661  …  3033.5532438752853, 3248.2427166645057, 3210.9902719047514, 3277.5308742897955, 3289.6449640286737, 3294.197028486097, 3298.9501440754834, 3313.047035157849, 3313.8650298737884, 3316.8143902164024], [34195.564496956766, 34469.767304992485, 34177.57155544327, 34434.36454077261, 35299.36946479525, 35444.67518322646, 35783.27145905632, 36822.49662016125, 37531.603817992334, 37317.351336454434  …  36410.595452782625, 38206.82911263397, 39590.294415154625, 40501.19261603309, 41606.09962529606, 42733.61183532848, 42538.607869147585, 42672.968682611754, 41027.61617154768, 40775.73502334759], [34195.564496956766, 34435.040698134064, 34108.74177641055, 34330.39653293806, 35157.334722634936, 35266.4906748571, 35567.51611669139, 36563.60207552333, 37230.17821475605, 36980.35297730525  …  36009.12099855878, 37747.481737346134, 39074.90835834603, 39933.67669500196, 40981.77261602511, 42049.95975805666, 41815.905497767904, 41905.72306676338, 40249.36328002672, 39961.95974249868], [33097.63671130043, 33065.451789820625, 32597.322696538627, 32672.417894665432, 33508.16013407128, 33363.808009311964, 34302.057754151036, 34318.147999461326, 34898.963349758924, 34577.07812684092  …  34333.03294021204, 35318.37777394362, 36849.54347009141, 37253.349683859895, 37884.48914037402, 38876.78201750815, 38990.84017683779, 38756.34277688181, 37677.81247341412, 37065.61857858138], [33097.63671130043, 33032.139962248926, 32531.6753606938, 32573.76974927496, 33373.332714854754, 33196.08426246602, 34095.23339425325, 34076.86122874648, 34618.681133975246, 34264.826099952414  …  33954.46633095687, 34893.75724126192, 36369.83648167919, 36731.34359732898, 37316.00735487188, 38254.831481561625, 38328.41199525352, 38059.51677672597, 36963.10201153857, 36325.8873597241], [29576.14777688488, 29605.974349916614, 29747.085804690116, 29896.60218340367, 30110.243272995063, 30303.6058557004, 30500.974491357923, 30908.372384630387, 31003.292633452857, 31082.548693641078  …  31633.80615535959, 31986.228259549123, 32072.921479582234, 32420.366155653624, 32474.86197711966, 32565.61171361927, 32675.60376785701, 32673.042930908487, 32743.171003196094, 32764.34206980003], [34346.71094688274, 34381.34847608992, 34486.49967800861, 34676.08590810341, 34982.667862914604, 35170.06359641693, 35394.799811109166, 35986.77187065241, 35963.849705657165, 36034.74223197631  …  36971.483253484745, 37300.35411615868, 37250.05881138899, 37702.20137443695, 37652.73761641427, 37566.282961173034, 37762.444088857505, 37654.328864203395, 37804.19770575261, 37858.77662909598], [28335.329312139656, 28363.904560528423, 28450.652136986064, 28607.05687312196, 28859.980673137903, 29014.578294688632, 29199.981044358497, 29688.345804427787, 29669.4354902876, 29727.92036478197  …  30500.712419476236, 30772.024109537175, 30730.531518712167, 31103.539823344658, 31062.733241463902, 30991.409931042235, 31153.23882230859, 31064.0459881252, 31187.68469651545, 31232.711184461503], [978.0617717704337, 979.0481149238881, 980.7694155355957, 981.6536653997443, 988.3587539172854, 996.6170853868558, 1001.6887226633481, 1022.2143806447572, 1023.1478841727718, 1027.8247631868526  …  1055.2094418311392, 1062.5459836288762, 1062.7772094816482, 1079.0993016384164, 1078.3415206153534, 1075.6934560833486, 1083.8492812722066, 1081.3958426401766, 1086.365715786615, 1081.6210141809786], [0.0019383188997990075, 0.0020497954811358188, 0.007290107646310995, 0.0035224469394072866, 0.008513404226309529, 0.008651759033972528, 0.004069054715025278, 0.00477921145328386, 0.0021542326437549164, 0.0007768968069488125  …  0.0012677943844039419, 0.0051785198295437596, 0.004504246181846749, 0.0027717059591831905, 0.005151071602359725, 0.003318971067896914, 0.007502554490973257, 0.005399364823022168, 0.005027815670034075, 0.001427867497377333], [2.3548476e6, 2.376637533494915e6, 2.375593984064073e6, 2.375464630435148e6, 2.3874073660577084e6, 2.390319625317714e6, 2.39943726754347e6, 2.396339583185188e6, 2.3997567540764967e6, 2.4125371660692305e6  …  2.4175803208178547e6, 2.431338872141019e6, 2.4617931714370335e6, 2.45077982413715e6, 2.4815113924474427e6, 2.498188852227642e6, 2.4919068222396807e6, 2.484716952440821e6, 2.439259736023082e6, 2.4201480489449217e6], [0.0016459319014481277, 0.0024812636400377824, 0.002421673021296292, 0.00231786638831355, 0.0028361414833700666, 0.002944697426721897, 0.0031952534203719624, 0.002941389169359878, 0.002913325161490189, 0.0032393073810100892  …  0.0029567418703733803, 0.003416452303931613, 0.0044989152744169475, 0.0037894179189816757, 0.004860467247228378, 0.005237841657133904, 0.0047748974708216535, 0.004261504866579133, 0.002261922847674895, 0.0013565788180846202], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.6059091613046 258.5036962251161 … 137.13538556670352 505.41380923410765; … ; 558.9048564547179 296.34030415169866 … 150.0718424997409 536.6112586586819; 567.0224904243262 299.9943299762391 … 150.81756637224245 538.6374730377835], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.0834390395163 258.24326637797435 … 136.99722836422478 504.904629508617; … ; 548.302989682779 290.7190246307063 … 147.22512957172302 526.4322791652686; 555.7062287768424 294.0072405960062 … 147.8076486512929 527.8877008841712])])

Now, apply a shock to the model and simulate it again Here, we do this by overloading the centralbankrate function with the wanted behaviour

import BeforeIT: central_bank_rate
+data = Bit.init_data(model);

Simulate the model for T quarters

data_vec_baseline = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72421.99999999999, 72448.58490719223, 72443.1781734764, 72698.52438274003, 72737.817885501, 72762.24125037965, 72929.34714814986, 72944.24177792823, 72683.96742329875, 72388.88313496008  …  72452.68486428603, 72596.56293106369, 72906.71544721426, 73069.91541533258, 73252.81543498056, 73739.19194261673, 74089.80872403622, 74177.93656190544, 74253.78394576492, 74265.95448630504], [72421.99999999999, 72375.59649670316, 72297.28577330911, 72479.0250293069, 72445.14134863042, 72396.4569931385, 72489.61943123599, 72431.37958789636, 72100.22448408337, 71735.16753180294  …  71653.79921709334, 71723.75978534766, 71957.61655456103, 72046.03592764027, 72153.6085496172, 72559.51277245396, 72831.07264539767, 72844.24222619756, 72845.2638450458, 72783.80344880585], [64900.920495538034, 64966.37094093037, 64945.22044766371, 65171.0785114404, 65198.088081263195, 65213.12049822655, 65365.50620367541, 65366.80382312546, 65116.10298740608, 64827.09865016365  …  64876.4046624161, 65003.3702392346, 65292.88545866005, 65434.17597051589, 65608.3760859957, 66061.4438023366, 66373.55173853131, 66437.58123226422, 66500.61416919502, 66503.33359435541], [64900.920495538034, 64900.920495538005, 64814.428089716435, 64974.30684767884, 64935.74929815252, 64885.286549822755, 64971.384688375496, 64907.21768792136, 64593.139441308645, 64241.67055596292  …  64161.05741719873, 64221.85739983447, 64442.9033286625, 64517.29095443986, 64623.87906287107, 65004.593202927535, 65245.90968249819, 65243.05588046156, 65239.16395359532, 65176.10383523067], [40512.94792630535, 40271.08996498962, 40429.366924747585, 40601.36944614105, 40693.87037005132, 40762.468268944125, 40831.44675626814, 40964.459807355255, 40992.09357977683, 40992.773715391566  …  41051.424461928436, 41119.44726915646, 41217.95726637218, 41332.372029194674, 41385.24035729534, 41490.61842928593, 41708.60242793214, 41846.0592878146, 41930.32223123079, 41979.08782915483], [40512.94792630535, 40230.518808921195, 40347.94673410153, 40478.78134111969, 40530.129672323594, 40557.55059567233, 40585.25342583904, 40676.44361775338, 40662.875926432454, 40622.58405872179  …  40598.77879585157, 40625.08250204338, 40681.38231084194, 40753.209351123754, 40764.22748723669, 40826.851753400915, 41000.00129142259, 41093.68122459795, 41134.94590757498, 41141.29682505845], [14866.888022051655, 14495.06804100784, 14605.659668009515, 14451.659432373639, 14379.340788609492, 14434.23625185593, 14281.52457028227, 14047.296516333745, 13998.024039443904, 14147.996741819628  …  14272.4377540225, 14306.310263915831, 14356.932619968979, 14169.372873153463, 14472.010423869577, 14755.983532565597, 14780.873129999245, 14889.17117753729, 14898.695076412896, 14824.970759239559], [14866.888022051655, 14480.464967978878, 14576.245514755663, 14408.025398142889, 14321.482362458788, 14361.673665884176, 14195.414075139866, 13948.531664169977, 13885.602442416792, 14020.231733948176  …  14115.065453831015, 14134.310487372792, 14170.034214685917, 13970.826998892868, 14254.848347456453, 14519.917392565329, 14529.756024921959, 14621.468890533195, 14616.081714850885, 14529.103750679731], [15944.236265986638, 16305.4890224627, 15895.693581321137, 16160.072397993641, 16251.446172928052, 15920.255606525492, 16323.964771063986, 16837.00084568974, 16865.850581606897, 16040.725699214123  …  15953.674424530845, 15474.600103993935, 16017.348788106241, 15762.189871955505, 15548.60270597151, 16270.262213140202, 16460.23573939655, 16532.571573261514, 16471.497313173702, 16654.03707712667], [15944.236265986638, 16289.062038726277, 15863.68144508731, 16111.280136074845, 16186.054920849, 15840.222628265748, 16225.539376617759, 16718.621917938603, 16730.396759534422, 15895.86819870552  …  15777.764290329216, 15288.554386343505, 15808.835101749874, 15541.324926385563, 15315.285651183985, 16009.970651485108, 16180.587391737194, 16235.32150020855, 16159.049464435237, 16321.666766892966], [15944.236265986638, 15938.556482792908, 15970.357842357993, 16019.271564088005, 16044.741176940104, 16044.882849841877, 16085.068674194838, 16094.358108067581, 16057.850594940981, 16001.378081570205  …  16017.09642290284, 16035.07817665459, 16094.460821653089, 16133.23444868366, 16166.514533445086, 16255.017274912661, 16289.551466515131, 16347.107152441724, 16361.408842242672, 16392.275649593703], [15944.236265986638, 15922.49916566711, 15938.195340713315, 15970.90442348037, 15980.18164767235, 15964.223355921742, 15988.083704479719, 15981.20061207781, 15928.886020825423, 15856.875913959235  …  15840.486978188279, 15842.294026698659, 15884.943291615098, 15907.170305482627, 15923.925303502612, 15994.969601691977, 16012.80292999083, 16053.191667259327, 16051.049261817603, 16065.129401638318], [3173.2320350842087, 3154.673113834523, 3166.7631755720513, 3166.859759640676, 3180.9653934640046, 3185.3291833426306, 3197.8033046773407, 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16499.184664517983, 16527.09417673819, 16593.616618212258, 16609.61784219211], [3173.2320350842087, 3165.935356210743, 3174.083255432376, 3174.0756911640433, 3181.322867301406, 3143.3326271964543, 3137.7420201618315, 3178.3317453654827, 3192.5536141779526, 2588.6595836028882  …  3228.4062142753937, 3250.0717993989715, 3251.3250035434758, 3260.6543381950783, 3270.5865714469383, 3292.187068746814, 3301.2158509380206, 3301.239635434314, 3312.8821301049957, 3327.907822751579], [3173.2320350842087, 3162.745830981823, 3167.691009314234, 3164.492173921102, 3168.522118165183, 3127.5307281545647, 3118.822995259735, 3155.985277161207, 3166.9134256135435, 2565.282414515922  …  3192.808811739901, 3210.99732017039, 3208.9992871593436, 3214.9649860950303, 3221.5092594397174, 3239.5186788856886, 3245.1304113004862, 3241.88445778094, 3250.0400657197943, 3261.491628866917], [34195.564496956766, 32907.09252228949, 32931.37689607605, 33382.584511630936, 33795.7956855173, 34011.326655416175, 35009.557887616465, 35297.828953634555, 36179.3239616535, 35709.118325203344  …  37012.0596234439, 38562.061347328934, 38448.20609753945, 39010.0607646658, 38642.83176762909, 37105.15673507315, 37388.99555096904, 37251.09795804689, 37075.62037881488, 36427.948999675646], [34195.564496956766, 32873.94023394464, 32865.05681270434, 33281.79215334795, 33659.81090167916, 33840.34775695676, 34798.46765354648, 35049.654163969346, 35888.758852773244, 35386.64328741839  …  36603.95323978701, 38098.443138830844, 37947.68773502894, 38463.4390694917, 38062.97055005448, 36511.548650243116, 36753.78163350101, 36581.33575316164, 36372.33289937386, 35700.94397055717], [33097.63671130043, 31832.95744449344, 31778.85572049785, 31972.015915354164, 32309.39631416122, 32433.287064978562, 33224.24208246702, 33455.019497596724, 34436.32060414513, 34840.281597076406  …  34909.79769839942, 36431.372428182374, 36282.70066623986, 36726.251752305194, 36523.365983001524, 35032.027938626525, 35314.287942103896, 35112.48445878926, 35127.29758473624, 34904.19625628953], [33097.63671130043, 31800.887294772543, 31714.856684943014, 31875.482500392052, 32179.392383654438, 32270.241155244705, 33023.91641538778, 33219.801279559346, 34159.75398680357, 34525.6526829112  …  34524.8715030503, 35993.37074911886, 35810.37283177978, 36211.6315339122, 35975.30874961398, 34471.58575640272, 34714.321913176536, 34481.173804896505, 34460.96784229288, 34207.6012814836], [29576.14777688486, 29605.9743499166, 29606.82337470008, 29624.176671101082, 29707.75361319999, 29847.0343581692, 30013.02085363335, 30164.725956828348, 30227.274761255292, 30331.947253997176  …  30880.82021631925, 30889.10642078999, 30889.891869853, 30972.81758590048, 31061.688887572913, 31139.826383497442, 31218.414891090957, 31313.548857445498, 31465.19086590722, 31521.636087099185], [34346.710946882755, 34381.34847608994, 34279.20037656289, 34291.71864694793, 34355.71617782144, 34577.44226554139, 34770.90199091198, 34809.96999438877, 34868.762027943056, 35082.76011576905  …  35880.886226676455, 35760.689282587016, 35818.63132197287, 35957.319947500735, 36116.38841689269, 36247.04091240261, 36227.884830001305, 36441.174877113284, 36665.761934948925, 36641.027015867854], [28335.329312139667, 28363.904560528437, 28279.634481708243, 28289.961797017328, 28342.758442241065, 28525.67789913909, 28685.278189119963, 28717.50848755508, 28766.010704674707, 28942.5547207843  …  29600.99232839549, 29501.832324447678, 29549.633313880237, 29664.048574260123, 29795.27678619487, 29903.062404778608, 29887.25903123256, 30063.218928332062, 30248.498626666464, 30228.092827731158], [978.0617717704336, 979.0481149238875, 970.8778465013482, 969.8027140960004, 969.9755719684879, 976.0536484460022, 980.8545630628927, 983.2171973346318, 985.2580305002805, 995.9538004519975  …  1027.8337589630662, 1026.5780178981574, 1037.2159084670702, 1040.1150910765764, 1048.0720786221545, 1050.3153431930261, 1041.917743245886, 1046.5467804946704, 1050.9147627816085, 1048.597927784884], [0.0019383188997990075, 0.005122000450098918, 0.001975893199044121, 0.0013513059669898464, 0.0025532309336269154, -0.002230889767642741, 0.004127951739481883, 0.004552842508664501, 0.004376015426513558, 0.0015706279523846067  …  0.0026299735854800055, 0.004531280062137322, 0.003495689615087949, 0.0027431374084148086, 0.006088703161976294, 0.007603404251185442, 0.006629451483911808, 0.006646110511273395, 0.007747490185630035, 0.005766720624261756], [2.3548476e6, 2.347994899465915e6, 2.355540098952287e6, 2.373607073759298e6, 2.371083840568892e6, 2.377156233580574e6, 2.3889419789059153e6, 2.3761161409542775e6, 2.4118164486844833e6, 2.4368407411811654e6  …  2.455145581777974e6, 2.4819536481337547e6, 2.4748007279225443e6, 2.4773420273708347e6, 2.457836949020113e6, 2.442669561974865e6, 2.448092862961278e6, 2.4492719610934393e6, 2.45257105702087e6, 2.4334952781477575e6], [0.0016459319014481277, 0.0013553869220913992, 0.0016164624146253071, 0.0022822203260439745, 0.0020672917317138963, 0.0021149508262642896, 0.002540763179356779, 0.0019290629639705469, 0.0033644925440202093, 0.004172004842384672  …  0.004416731507036507, 0.005267736283257837, 0.004674497896644861, 0.004492382630672169, 0.0035250952069484165, 0.002831622402161816, 0.0029925035482987507, 0.0029708483007389788, 0.003061184607641516, 0.0022018596797915343], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086172; 518.605909161305 258.503696225116 … 137.13538556670352 505.41380923410753; … ; 539.840511638757 275.2449442903339 … 144.4028511851542 527.2565110287557; 543.5261257738902 276.09395806197955 … 145.08111992347125 530.2346762590386], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086172; 518.0834390395162 258.2432663779743 … 136.99722836422478 504.90462950861695; … ; 529.6002764423824 270.0238226713024 … 141.66367335895842 517.2549817523322; 532.6787891066955 270.5838565730906 … 142.18568642499338 519.6526015927773])])

Now, apply a shock to the model and simulate it again Here, we do this by overloading the centralbankrate function with the wanted behaviour

import BeforeIT: central_bank_rate
 
 function central_bank_rate(cb::Bit.CentralBank, model::Bit.Model)
     gamma_EA = model.rotw.gamma_EA
@@ -38,7 +38,7 @@
     end
 end
 
-data_vec_shocked = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72421.99999999999, 72493.2652911228, 72721.22558417902, 72822.30451206617, 72961.6380592912, 73475.67329718199, 73845.74892823336, 74187.01588429912, 74700.13418537156, 75143.94348039983  …  75803.41563625904, 75826.79939147201, 76072.21360273063, 76377.81930194674, 76360.82724284206, 76597.55079469453, 76880.1243054036, 77055.74227814635, 77181.36354433137, 77280.42384943443], [72421.99999999999, 72420.23186732958, 72574.77322784858, 72602.43142809863, 72668.06093294393, 73106.30253398874, 73400.49576404375, 73665.4158990092, 74100.19891167928, 74465.34800816032  …  74967.5837982344, 74915.16023988799, 75081.90628674328, 75307.58838607135, 75214.98258719429, 75372.14361599577, 75573.98263949856, 75670.30596415131, 75717.3101833276, 75738.11201652183], [64900.92049553803, 64966.37094093018, 65181.84173723354, 65265.647077250425, 65421.29473727362, 65896.49675244908, 66216.63846010673, 66535.78117290227, 67002.06179957282, 67406.20486151472  …  67979.27937415295, 67983.20804580854, 68204.14440673875, 68488.44504410676, 68453.85537443552, 68678.57049292249, 68928.3958543882, 69079.83677624949, 69199.57137451816, 69283.04850297452], [64900.92049553803, 64900.920495538005, 65050.57285066444, 65068.58988170898, 65158.05783330939, 65565.22739206135, 65817.38504031928, 66067.97609855927, 66463.95165128414, 66797.4858709842  …  67229.7188754607, 67165.86965618623, 67316.26353686715, 67528.76261357462, 67426.66005490556, 67579.85111571458, 67757.34871310405, 67837.80455898527, 67886.9246370361, 67900.3430233231], [40512.94792630534, 40538.66098817789, 40542.30124165655, 40623.66745625758, 40469.01530979195, 40638.77884730747, 40949.02651394521, 41030.25792479932, 41249.440558554255, 41377.67821515384  …  41858.9406444286, 41957.38840008682, 42042.97327630604, 42167.47671759698, 42301.38538742273, 42323.90219528811, 42524.67308921771, 42665.89196831979, 42690.412466779446, 42804.40581532953], [40512.94792630534, 40497.8202673733, 40460.65361401823, 40501.012026639895, 40306.17936563319, 40434.4829751573, 40702.12423601535, 40741.77911654811, 40918.15608790346, 41004.012636957115  …  41397.39105589183, 41452.949359131635, 41495.658270608714, 41576.612280250054, 41666.62515599503, 41646.804650489765, 41802.207460387865, 41898.773589412616, 41880.61799069812, 41950.14365809294], [14866.888022051655, 14923.765676684172, 15209.369268525757, 15261.679260828476, 15341.865559503403, 15395.059187920802, 15507.681679440147, 15585.739661907237, 15903.79858220266, 16240.296420073848  …  16490.38555400318, 16596.535981439683, 16806.308245478835, 16683.900092749485, 16734.329614445684, 16786.078269626625, 16887.80383331182, 16778.757490043456, 16657.862710399804, 16790.69573301388], [14866.888022051655, 14908.730711727323, 15178.739312143944, 15215.59952594389, 15280.134204183418, 15317.666433197102, 15414.178066338109, 15476.158201034606, 15776.071237945398, 16093.636674683139  …  16308.55747737038, 16397.001620200972, 16587.523892795834, 16450.119842935754, 16483.219944122426, 16517.534685655704, 16600.891390965902, 16477.081077057745, 16341.879696258164, 16455.598079275376], [15944.236265986641, 16073.12917161254, 16045.796243662626, 15886.436860925573, 15985.367230943191, 16194.68331818411, 16049.89605439693, 16210.419256253534, 16402.576401560185, 16377.97008100366  …  16997.386623652917, 16692.551374513045, 16170.82286319529, 17038.41147172013, 16564.169040903143, 16601.559388528258, 16818.22077593569, 16785.261017681878, 17164.15725907561, 16670.716376076554], [15944.236265986641, 16056.93627907605, 16013.48181757581, 15838.470789413957, 15921.046605747613, 16113.27075987089, 15953.123158098186, 16096.445748291011, 16270.84324913629, 16230.067058794793  …  16809.968196870745, 16491.86265370512, 15960.311252864249, 16799.663692834838, 16315.612742391528, 16335.967736559833, 16532.49050299053, 16483.4676734579, 16838.570343142044, 16338.013191372931], [15944.236265986641, 15960.023677397225, 15985.553707288933, 16010.280933626906, 16014.016535995986, 16071.985110811029, 16157.837772507992, 16229.184288989427, 16315.332397026154, 16406.863400175273  …  16575.56615694192, 16596.59491975417, 16622.150644560566, 16687.0927624731, 16704.829992392206, 16741.794978231406, 16788.831554487522, 16817.306315153717, 16870.311503151926, 16905.36611730764], [15944.236265986641, 15943.944733121434, 15953.360602884093, 15961.940938516218, 15949.580634048501, 15991.189370669945, 16060.414041296819, 16115.078846338842, 16184.299922804632, 16258.699453736725  …  16392.79885271205, 16397.05984992067, 16405.76365368482, 16453.267775929853, 16454.162983384886, 16473.95983810248, 16503.60058467186, 16514.93680726565, 16550.29854765156, 16567.979947515894], [3173.2320350842083, 3176.140308438829, 3170.5239576983004, 3175.682809823531, 3153.410186107544, 3132.9136351367288, 3156.1959564327717, 3169.060941295984, 3174.3400490132917, 3191.9646225016986  …  3255.2741733333446, 3273.450558287896, 3268.7539274691158, 3278.477681827882, 3306.020977176084, 3291.561473879928, 3301.4905662212304, 3298.833881520313, 3319.3901471260024, 3344.140573338507], [3173.2320350842083, 3172.940502219009, 3164.138879604842, 3166.094440191769, 3140.7217497558327, 3117.1641135808186, 3137.1656634666883, 3146.779655000001, 3148.846138100482, 3163.1392423039824  …  3219.380516395414, 3234.0950043985217, 3226.201321527609, 3232.538583223499, 3256.411948503443, 3238.9030922819024, 3245.4004593555837, 3239.521958512704, 3256.424632159541, 3277.4004169020986], [34195.564496956766, 34189.85715763445, 33754.98392649195, 34424.66623906128, 35033.8070766646, 36149.01830304069, 36905.298637115826, 37205.454806588445, 36650.882591294205, 36775.7465410563  …  36202.55565707132, 36671.31094107127, 36991.36038229074, 36957.04789253851, 37321.19319159476, 38505.00769027499, 38231.28659232831, 38087.15970627897, 38921.917495952235, 38498.56624655499], [34195.564496956766, 34155.41254657707, 33687.00519133974, 34320.72751340231, 34892.84088292022, 35967.29297981332, 36682.77802852254, 36943.867729004865, 36356.53027829966, 36443.63920232584  …  35803.375113792936, 36230.42456498678, 36509.80722273352, 36439.193683350895, 36761.16403390911, 37889.004797885056, 37581.76271589658, 37402.36539252706, 38183.60760468001, 37730.23720131174], [33097.63671130043, 33232.147702985654, 32831.22509615751, 33374.14530500644, 33868.41711761155, 34901.86635927086, 35566.1539566645, 35844.85576524922, 35506.5639482394, 35627.747776887474  …  35745.852842896806, 36090.98730563855, 35939.25116453991, 36469.01687265806, 36560.24999152424, 37618.99674902312, 37581.85998538957, 37261.32790417736, 38252.98638787551, 37483.96032153998], [33097.63671130043, 33198.66793742382, 32765.106707228788, 33273.37842730108, 33732.1401255402, 34726.41061404962, 35351.70772493017, 35592.834895869164, 35221.40194160508, 35306.007564600215  …  35351.708045691325, 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29582.8293554126, 29641.212661353602, 29694.832717805966, 29748.461839680673, 29903.525890675784, 30072.246306450026, 30183.338502162565  …  30454.460825532846, 30606.70708148772, 30722.493468002514, 30694.971723921775, 30695.80204493092, 30767.961857758793, 30810.818799904155, 30972.218398377227, 31111.10950498537, 31363.787351756684], [34346.71094688274, 34381.34847608992, 34348.82567808436, 34290.62484671297, 34390.94072726797, 34469.65299216383, 34640.98269290024, 34819.75850586866, 35030.61407299247, 35116.341217160254  …  35347.44908257009, 35594.83615885735, 35783.851836943104, 35641.11712787079, 35601.21704942778, 35681.88897259552, 35770.90665594722, 36163.65601993126, 36408.57689123864, 36855.57320446327], [28335.329312139656, 28363.904560528423, 28337.073921837426, 28289.05943434548, 28371.8179702402, 28436.753968047316, 28578.09744337526, 28725.58380797476, 28899.5353092114, 28970.258437977354  …  29160.917668293197, 29365.00691238436, 29520.9409548608, 29403.187758867996, 29370.271015718667, 29436.823691249567, 29510.26144708811, 29834.27158543386, 30036.32626119724, 30405.0890156898], [978.0617717704337, 979.0481149238881, 977.4914413575634, 976.2840244856071, 980.5574513963136, 980.8627077476258, 986.4853655136219, 993.1287469910499, 998.0241707999853, 998.5791524836719  …  1000.6636132714345, 1003.0331605431847, 1006.316370679145, 1002.99607643918, 1002.6914109570766, 1005.2017494722955, 1006.2529547881196, 1019.2310720271887, 1031.2446512842916, 1045.1250549962933], [0.0019383188997990075, 0.005790830824352167, 0.005050845392942094, 0.0027526015568375417, 0.005184994811318289, 0.003449238890251838, 0.005248362133795759, 0.005894413236208651, 0.004876003487249259, 0.004159579067936514  …  0.0005985884261228147, 0.0015692868015186878, 0.003340092740924394, 0.0006761446447620667, 0.004559384482941908, 0.0026019418538172356, 0.0010862476827879775, 0.0037089611701299763, 0.007283356886845116, 0.010069194178136787], [2.3548476e6, 2.36211342082883e6, 2.366735399276434e6, 2.381416013539383e6, 2.3622672348040813e6, 2.373431246504549e6, 2.3857134482584773e6, 2.377987366947936e6, 2.373522735178423e6, 2.367223036289935e6  …  2.335378502098738e6, 2.3106570122220158e6, 2.3122796987729096e6, 2.3073392548956797e6, 2.323783123097426e6, 2.3157833840274788e6, 2.338028495205493e6, 2.3475926555120703e6, 2.3303558299712683e6, 2.3472860372078735e6], [0.0016459319014481277, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01  …  0.007413310069418032, 0.005859288137239107, 0.005571229624351785, 0.004964133212721521, 0.005402239970737166, 0.004723395683084625, 0.005343467640972192, 0.005435347716412342, 0.004477898466967431, 0.005092045909748898], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.6059091613046 258.5036962251161 … 137.13538556670352 505.41380923410765; … ; 547.5438102859658 267.3147967091517 … 142.95761849589076 531.5027126900022; 549.4652585650516 268.25017615302784 … 143.6424756189032 534.166190257831], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.0834390395163 258.24326637797435 … 136.99722836422478 504.904629508617; … ; 537.1574512099746 262.24410206739014 … 140.2458552899998 521.4206372100906; 538.4993925947716 262.89661570067057 … 140.77575363653494 523.5056530229205])])

Finally, we can plot baseline and shocked simulations

Te = T + 1
+data_vec_shocked = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72421.99999999999, 72493.87433558593, 72486.24720209323, 72541.5412661788, 72754.28572344196, 72998.01305529532, 73170.75661191114, 73382.37517895488, 73609.90523631586, 74133.45961287666  …  74769.13109082599, 74907.4187704306, 74989.95288960553, 75103.9660328711, 75307.35322646733, 75807.70804376669, 76240.29612968545, 76480.30168510508, 76697.62493163286, 76983.50330713866], [72421.99999999999, 72420.84029820995, 72340.26806575408, 72322.51589337844, 72461.54292461972, 72631.04354574412, 72729.5733159132, 72866.43252568031, 73018.72586125853, 73463.98942400012  …  73944.70359316505, 74006.83301126306, 74013.73443275953, 74051.58476448896, 74177.31664434508, 74594.9367648712, 74945.0247146662, 75105.21159931536, 75242.7476088493, 75447.11721897505], [64900.920495538034, 64966.37094093037, 64966.15376899745, 65007.0854972208, 65198.86960398631, 65436.744381784156, 65594.19912701036, 65809.63945903411, 66013.40007440967, 66499.00744549806  …  67051.9644838732, 67170.38822976784, 67231.97627508709, 67340.81875714153, 67520.7897651046, 67993.86973976619, 68391.25580683784, 68612.19419039675, 68792.25641894157, 69049.71345345341], [64900.920495538034, 64900.920495538005, 64835.31925369918, 64810.8089791434, 64936.52767624789, 65107.78625008512, 65198.69870704491, 65346.93979973943, 65483.230113232414, 65898.48099890946  …  66312.62884513829, 66362.82208918467, 66356.7510802321, 66397.21724045844, 66507.59571141242, 66906.10420128373, 67229.33431412508, 67378.5700294467, 67487.3360392526, 67671.66472113843], [40512.94792630535, 40553.03495366472, 40500.41394256369, 40574.11157635979, 40664.3696946115, 40640.12499826774, 40773.452005375584, 40758.89602325946, 40899.92521308691, 41070.74750597133  …  41594.68260254624, 41709.39564236134, 41856.82901116012, 41918.68710915944, 42006.95808882685, 42075.68074088445, 42278.561625720446, 42406.55713553605, 42656.19328163518, 42780.860228467216], [40512.94792630535, 40512.17975179201, 40418.850671227134, 40451.6057712867, 40500.74769931089, 40435.822358860125, 40527.60835447075, 40472.32512789802, 40571.4477867169, 40699.85369859063  …  41136.046804674515, 41207.938131802664, 41311.93722964514, 41331.3087929525, 41376.615933509005, 41402.55424149129, 41560.27726532429, 41644.10150964636, 41847.04790931053, 41927.06797855553], [14866.888022051655, 14842.869255898924, 14837.00187963905, 14940.98960002584, 15057.26514831584, 15003.550710903517, 14938.241876353119, 14801.275368351737, 14958.897397012424, 14874.538839226356  …  14999.257394007405, 14789.973261458828, 14700.317191944758, 14666.044915922084, 15016.750287796129, 15112.622071061158, 15044.255214358893, 14874.05937020984, 14954.361476799146, 15209.639722582546], [14866.888022051655, 14827.915790134575, 14807.121829231513, 14895.878126516556, 14996.678945066156, 14928.126114870547, 14848.171702248199, 14697.20938156987, 14838.758788134683, 14740.21271471862  …  14833.870956319735, 14612.1585734563, 14508.948609737587, 14460.539511000945, 14791.414029877546, 14870.850429765746, 14788.663424601573, 14606.628788415917, 14670.692179233587, 14906.09574404306], [15944.236265986638, 16094.372631952237, 15998.355595194778, 16018.547734410358, 15832.16052448163, 16199.956565579258, 16069.291688617843, 16238.54319558403, 15993.057256065415, 16349.767343061985  …  16595.420291571325, 16544.129497761132, 16566.6893392203, 16550.27721203932, 16285.832234993573, 16615.30316029238, 16817.626760392075, 16911.69604258627, 16797.573598256196, 16760.53689054121], [15944.236265986638, 16078.158337667115, 15966.136709229813, 15970.182779268638, 15768.456360015534, 16118.517498037552, 15972.401846284547, 16124.371951586925, 15864.613053297538, 16202.119008714635  …  16412.434069530147, 16345.225201390027, 16351.024343062894, 16318.369329613253, 16041.452564119925, 16349.491635542017, 16531.90657937199, 16607.62943916467, 16478.94040814535, 16426.04112713014], [15944.236265986638, 15960.081690744853, 15959.476028334851, 15973.783969506196, 15995.424705127338, 16028.067009002698, 16062.294460576462, 16106.747682184863, 16167.097480236962, 16268.376050520808  …  16448.33196305167, 16493.443432507804, 16533.099951783974, 16545.25358027804, 16601.459890269372, 16666.274525699842, 16727.656293365664, 16809.03138980562, 16874.336892337706, 16940.809337658004], [15944.236265986638, 15944.002688023458, 15927.335441437786, 15925.554170030111, 15931.063611482234, 15947.49205027209, 15965.446808050585, 15993.503076589786, 16037.255517335714, 16121.462729004035  …  16266.967582281715, 16295.148517013846, 16317.872221936335, 16313.41609061875, 16352.3440179292, 16399.647561337766, 16443.464650140566, 16506.810662290485, 16554.247579225907, 16602.715815403397], [3173.2320350842087, 3176.198321786469, 3172.9191980143123, 3177.218711382464, 3182.0268861175564, 3164.786243507184, 3171.4066349832824, 3158.3617354963517, 3165.199467488707, 3174.0997410609816  …  3232.460375354532, 3248.7365537859046, 3266.082135186305, 3263.6760484791275, 3276.7573326838246, 3270.1464605332353, 3258.5530933278665, 3291.4508974426244, 3299.4960901339978, 3321.4703605898794], [3173.2320350842087, 3172.9984571210334, 3166.5292961766804, 3167.625704388369, 3169.2233042074754, 3148.8764946386186, 3152.284629185358, 3136.1556740294263, 3139.778966849362, 3145.435691598597  …  3196.8182703887205, 3209.678127749447, 3223.5643106100256, 3217.944355189007, 3227.587424327929, 3217.830676196019, 3203.19246528242, 3232.27171800408, 3236.907945554187, 3255.182641337893], [34195.564496956766, 33996.562731251375, 34030.34948319632, 35036.62613141018, 34897.21254075627, 35814.75317379978, 35256.38373063274, 34354.04346662303, 34944.118709836715, 36013.40823056984  …  35038.99336003314, 35258.27240186681, 36761.140330356415, 35935.627174808506, 37374.31184582877, 37798.40392231808, 37725.39407335089, 37010.29801801848, 37843.71526803669, 38325.49357529545], [34195.564496956766, 33962.312854886986, 33961.81619283266, 34930.839709366286, 34756.79596503581, 35634.70824029006, 35043.805259403496, 34112.5043191962, 34663.473840175626, 35688.185270032074  …  34652.64261347601, 34834.37449510159, 36282.58417313322, 35432.08544586998, 36813.48560764691, 37193.705272958934, 37084.46497097138, 36344.8653150169, 37125.85779588651, 37560.618600508154], [33097.63671130043, 32992.96523718128, 32879.873698500676, 34028.7337760274, 33696.722184723214, 34660.37239325516, 33866.61268906791, 32770.382874863135, 33186.093339685416, 34175.00230595301  …  33459.222557331814, 33394.35203301729, 34895.02298307614, 33966.670379058036, 35376.49923097793, 35794.30185078922, 35625.54154413676, 34722.30888124592, 35554.21869309452, 36093.027109748014], [33097.63671130043, 32959.72643627069, 32813.65733676707, 33925.99049305979, 33561.136044808576, 34486.13066631423, 33662.41384649349, 32539.978254570484, 32919.56760706608, 33866.38126805601  …  33090.29085083502, 32992.86339048731, 34440.75992281934, 33490.718314947495, 34845.6514908083, 35221.66481488664, 35020.28752560292, 34098.013452928775, 34879.79068372683, 35372.70623126203], [29576.14777688486, 29605.9743499166, 29612.564790327073, 29633.273005083916, 29674.341868085794, 29773.625481476625, 29870.983068472633, 30003.480983329217, 30127.34421170735, 30332.258497651866  …  30625.729868766786, 30682.294487021252, 30756.25709122765, 30818.01506013272, 30915.436507999635, 31072.429444338646, 31244.128999715023, 31365.028578283276, 31500.228877017078, 31609.812156057305], [34346.710946882755, 34381.34847608994, 34374.83485746372, 34395.28643994327, 34536.61302984432, 34672.0196003957, 34727.066727501515, 34812.590846111976, 34891.77067014702, 35164.057039929074  …  35422.64049810914, 35485.25213525931, 35473.49565430905, 35514.25682892465, 35598.405962188765, 35901.87393528436, 36118.79048485176, 36216.13101077588, 36264.734525957174, 36407.264981666995], [28335.329312139667, 28363.904560528437, 28358.530959281055, 28375.40309371654, 28491.99462038705, 28603.70225299078, 28649.114999400255, 28719.67063626184, 28784.992360731467, 29009.623009264687  …  29222.94903787731, 29274.60234931566, 29264.903494604572, 29298.530624365816, 29367.951926628415, 29618.30675584771, 29797.258442801103, 29877.562372389664, 29917.65927163918, 30035.24396281362], [978.0617717704336, 979.0481149238875, 978.7541212066022, 978.5260521936318, 987.9147060561345, 991.0992999117552, 996.14933103619, 993.5676295929765, 994.2851925553587, 1002.6919079171525  …  1003.594116997254, 1002.8416074873062, 1002.2235295503608, 1008.5468680841508, 1006.9472949162606, 1019.566360143165, 1028.3363222710766, 1031.0346013375283, 1027.2930159673592, 1032.6363157290837], [0.0019383188997990075, 0.0005034781538029165, 0.002013937294989221, 0.006044192248144897, 0.0024582609795036348, 0.001318291702260499, 0.0032571790103059772, 0.008702925713858667, 0.0068366052615278505, 0.004008428975254219  …  0.001561867066953937, 0.004251765408784358, 0.0029783942023824395, 0.002119639034232401, 0.0049097120340051426, 0.004027807911864079, 0.008335145518057141, 0.00493765235074517, 0.006100605224708344, 0.004772616349755898], [2.3548476e6, 2.348332556129432e6, 2.3450186311406298e6, 2.352444174145798e6, 2.358129295310786e6, 2.3875896978897303e6, 2.38856540473192e6, 2.37542088301649e6, 2.3758498604940698e6, 2.393125369367983e6  …  2.3827075959981806e6, 2.3643169239712367e6, 2.386692280822981e6, 2.367848706350676e6, 2.403420470474168e6, 2.422247470239868e6, 2.4293563067954606e6, 2.4305781768537792e6, 2.4228712842697515e6, 2.4337180558423456e6], [0.0016459319014481277, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01  …  0.008205680485461385, 0.006936577340660266, 0.00742224494930647, 0.006144816441601133, 0.007280208036753789, 0.007604156152150216, 0.007521139495594058, 0.00712710647137193, 0.006427581012141878, 0.0065015893649494], [518.0834390395164 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29642.40959866352, 29918.38136329532, 30163.732542775826, 30441.439759450514, 30622.014809670483, 30794.214605334124, 30922.730896737627, 31045.102374248323, 31178.852406373826, 31285.923397298913], [978.0617717704336, 979.0481149238875, 984.4223481357992, 992.5385083850049, 1001.3150602878164, 1004.3810525608626, 1008.6041194296762, 1009.162290516666, 1010.8125161978862, 1016.8036655088634  …  1014.0180378845857, 1027.4009212847159, 1038.826134120326, 1048.4191589154714, 1050.116544430378, 1055.0759618426225, 1058.4348183872526, 1063.519877737412, 1067.1776296774397, 1075.7855205607027], [0.0019383188997990075, 0.003934756825157759, 0.0016237988162384198, -6.18631752219434e-5, 0.006355305503540132, 0.0076682154591452, 0.0052896358183069925, 0.005570993495327192, 0.0028742965169932244, 0.009247653929747468  …  0.008727527899742515, 0.005301326844600718, 0.004415096752066416, 0.005646873507775929, 0.00729671554534872, -0.00023994581847008867, -0.0010638106825096028, 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148.39790006519974 539.4465232030852; 572.4020101786851 292.5719956079231 … 148.78361715462046 540.5281913263592], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086172; 518.0834390395162 258.2432663779743 … 136.99722836422478 504.90462950861695; … ; 565.3240344466191 286.73303629160154 … 145.5829401528686 529.2137615737367; 560.9783876167609 286.73303629160154 … 145.81429165340205 529.7406854616182])])

Finally, we can plot baseline and shocked simulations

Te = T + 1
 StatsPlots.errorline(
     1:Te,
     data_vec_baseline.real_gdp,
@@ -56,4 +56,4 @@
     titlefont = 10,
     xlabel = "quarters",
     ylabel = "GDP",
-)
Example block output

Note that, importantly, once the function central_bank_rate has been changed, the model will use the new interest rate in all the simulations, unless the function is changed again. To restore the original interest rate, you could close and restart the Julia session.

+)Example block output

Note that, importantly, once the function central_bank_rate has been changed, the model will use the new interest rate in all the simulations, unless the function is changed again. To restore the original interest rate, you could close and restart the Julia session.

diff --git a/previews/PR39/examples/scenario_analysis_via_shock-6b21c134.svg b/previews/PR39/examples/scenario_analysis_via_shock-6b21c134.svg deleted file mode 100644 index e61476f..0000000 --- a/previews/PR39/examples/scenario_analysis_via_shock-6b21c134.svg +++ /dev/null @@ -1,48 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/previews/PR39/examples/scenario_analysis_via_shock-e04e2cb5.svg b/previews/PR39/examples/scenario_analysis_via_shock-e04e2cb5.svg new file mode 100644 index 0000000..fa993f0 --- /dev/null +++ b/previews/PR39/examples/scenario_analysis_via_shock-e04e2cb5.svg @@ -0,0 +1,46 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/previews/PR39/examples/scenario_analysis_via_shock.html b/previews/PR39/examples/scenario_analysis_via_shock.html index 877e0d7..7856181 100644 --- a/previews/PR39/examples/scenario_analysis_via_shock.html +++ b/previews/PR39/examples/scenario_analysis_via_shock.html @@ -24,7 +24,7 @@ "Y_EA" => 2.35485e6 "D_I" => 54049.0 ⋮ => ⋮

Initialise the model and the data collector

T = 20
-model = Bit.init_model(parameters, initial_conditions, T);

Simulate the model for T quarters

data_vec_baseline = Bit.run_n_sims(model, 4)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72422.0, 72476.10634292384, 72853.52853121767, 73012.00350649771, 73486.12789068713, 73827.07628549164, 74168.98907436302, 74655.43578962133, 75409.82703719416, 76082.64792855683  …  77515.10758222666, 77865.6463612359, 78132.52486185907, 78129.38896327783, 78272.85486795282, 78404.11874007272, 78623.87426403702, 78720.32733917232, 78727.50194220823, 78718.26452468739], [72422.0, 72403.09020593275, 72706.80973165562, 72791.55766253285, 73190.44036466138, 73455.93897856829, 73721.78693274478, 74130.54240031967, 74804.19204448596, 75395.575384729  …  76660.40210093233, 76929.494863929, 77115.39643450282, 77034.61448200636, 77098.31897473258, 77149.810097736, 77288.10745774038, 77304.96234597621, 77234.11988041496, 77147.2572139711], [64900.92049553802, 64966.370940930196, 65305.93714682046, 65470.931092461746, 65907.490697799, 66211.95494194585, 66524.57748186737, 66985.43690971668, 67697.92694413371, 68339.18934761803  …  69608.46137202764, 69913.61691604379, 70140.61667706029, 70114.25726185058, 70246.5557884335, 70353.1291958044, 70556.61318999103, 70633.41232039164, 70630.9628932757, 70613.22807655427], [64900.92049553802, 64900.920495538005, 65174.4183460756, 65273.254080919956, 65642.29747793054, 65879.09973651612, 66123.46734282648, 66514.47035997374, 67154.22813056994, 67722.04494023239  …  68840.93701019885, 69073.06989413298, 69227.52683053772, 69131.79342023969, 69192.45981495966, 69227.62023806354, 69357.90372996876, 69363.44733768098, 69291.16408866695, 69203.97574850333], [40512.94792630534, 40414.48114407223, 40529.225574578486, 40442.31750154947, 40620.550512234666, 40800.74957687054, 40950.686411445684, 40970.836159666454, 41071.65811013519, 41151.000970464316  …  42024.1880098394, 42262.23279517781, 42485.64893404325, 42635.4944986129, 42707.76941505745, 42804.4235305962, 42890.35971056284, 43008.7504258248, 43080.40379015513, 43132.555860020795], [40512.94792630534, 40373.76552839491, 40447.604279860614, 40320.2096235931, 40457.10483300885, 40595.63945900732, 40703.77412515883, 40682.77513869847, 40741.80145434713, 40779.38242553159  …  41560.81635289043, 41754.12871642672, 41932.57118106018, 42038.07204346726, 42066.91112284386, 42119.63860822932, 42161.6813116952, 42235.46756723675, 42263.21156827742, 42271.74470018223], [14866.888022051655, 15059.716310462483, 15398.212407411242, 15531.815046749538, 15697.823598035438, 15729.534731334556, 15858.20702738373, 16179.82440347688, 16681.168814574452, 16935.31131978639  …  17374.149406732344, 17519.86869784693, 17639.37685241709, 17693.68282453936, 17778.389609361304, 17907.45405001211, 18004.556914874, 17974.079995238546, 17985.874692819834, 18082.05897698685], [14866.888022051655, 15044.544381882713, 15367.20214222073, 15484.91968829, 15634.659967607155, 15650.460529116792, 15762.589920647099, 16066.066004244196, 16547.198217501005, 16782.3751675125  …  17182.576677312994, 17309.233429528223, 17409.747620939346, 17445.753169731455, 17511.61312912335, 17620.970702732335, 17698.671587877747, 17650.912085880664, 17644.70061807733, 17721.18914560558], [15944.236265986641, 16237.798487344815, 15801.69006508086, 15946.19452756357, 16085.3962217746, 16181.910987438901, 16217.885816068125, 16309.87407439871, 16525.87017360305, 16445.28138785891  …  16883.82389203054, 17371.47896714822, 17397.927844050955, 16744.605785707256, 17353.680192130283, 17142.74140322249, 17095.385632058336, 17499.363859282766, 17167.591990197812, 17719.93797838971], [15944.236265986641, 16221.43969851612, 15769.867241339294, 15898.048029154674, 16020.67310803215, 16100.56263711919, 16120.100024998565, 16195.201311572455, 16393.1468183678, 16296.770503646101  …  16697.657642949103, 17162.627736785547, 17171.441793349746, 16509.974907923748, 17093.27675725484, 16868.49192448414, 16804.946514399802, 17184.73118628948, 16841.939920846366, 17366.29511954899], [15944.236265986641, 15958.184556526323, 15994.484147297966, 16030.262588362708, 16088.541489148165, 16156.644813221867, 16240.804319370995, 16310.530968172974, 16437.315338103213, 16538.46600256049  …  16859.3623885141, 16959.761171751048, 17009.228201898688, 17046.554966096886, 17067.65899996013, 17106.99816035212, 17138.485774119636, 17173.306421129586, 17178.359777474438, 17184.627269772733], [15944.236265986641, 15942.10746507498, 15962.273057992936, 15981.862262449795, 16023.805719733753, 16075.423478901257, 16142.880341117172, 16195.853586805606, 16305.30318867839, 16389.113604651564  …  16673.46585951418, 16755.859880785792, 16787.802239252167, 16807.693077912325, 16811.547502014702, 16833.320501808394, 16847.314414001015, 16864.536151143653, 16852.503453955545, 16841.667789711006], [3173.2320350842083, 3174.3011875679294, 3162.135637117066, 3159.6007003639534, 3143.7867537925836, 3151.3409492861388, 3177.679197626059, 3168.580563715345, 3174.184890699557, 3166.0453515242743  …  3232.1134328451194, 3275.4289061499744, 3294.368598191502, 3338.5227242084748, 3327.454117399014, 3351.9544955617457, 3345.641526541795, 3364.146789970548, 3373.8485250787166, 3378.6844256951194], [3173.2320350842083, 3171.103234172552, 3155.7674521563167, 3150.060887599856, 3131.1370394279293, 3135.4987914768185, 3158.5193713928957, 3146.302654896347, 3148.6922259015723, 3137.4540380937947  …  3196.475153374405, 3236.0495672745346, 3251.4825407226745, 3291.7422548856375, 3277.523470298921, 3298.329946748935, 3288.8013245197717, 3303.660562849283, 3309.8499890873227, 3311.254865790303], [34195.564496956766, 33585.37964086074, 33877.283914428124, 34402.15661052422, 34404.96069953132, 35371.9088204242, 35517.95067786078, 36643.00544119641, 36986.5257738054, 36382.27549571651  …  37454.6041622319, 37834.54956121702, 37188.634592240254, 37168.38253670458, 36499.75478665604, 37119.869775016785, 36831.86378363489, 36826.425503722196, 36558.620355087936, 36618.960502443806], [34195.564496956766, 33551.54401137535, 33809.05888087717, 34298.28584840299, 34266.52480687694, 35194.09011703162, 35303.79508794025, 36385.37287744812, 36689.47782730542, 36053.72143962526  …  37041.61814601292, 37379.677973079866, 36704.513318990634, 36647.567037552966, 35952.05185486714, 36526.02631105834, 36206.115160627654, 36164.29876664782, 35865.13984960611, 35888.14339147804], [33097.63671130043, 32821.26923981568, 32752.883430280257, 33310.480179888866, 33322.603140888554, 34257.02783057632, 34375.74085839492, 35448.10428911682, 35855.39583492333, 34831.22124526877  …  36221.65788860721, 37122.48366015377, 36579.063360891996, 36112.776682285854, 36066.73913525163, 36570.37001877459, 36198.291777092716, 36588.29244489583, 36064.98888605208, 36835.248793153376], [33097.63671130043, 32788.203414235824, 32686.922812641442, 33209.90552690765, 33188.522350863444, 34084.813763706436, 34168.47222599958, 35198.87293164322, 35567.43227303475, 34516.67415158599  …  35822.26671823281, 36676.17299189095, 36102.877479836585, 35606.75267666865, 35525.53388935599, 35985.317448767804, 35583.30711685963, 35930.447260078385, 35380.87207639204, 36100.11514284343], [29576.14777688488, 29605.974349916614, 29664.3478121111, 29753.939965810645, 29915.6239190998, 30052.534781708087, 30200.882879018867, 30354.15571979131, 30626.78619032, 30858.94892246693  …  31404.24775912791, 31539.697292509456, 31601.82872792925, 31590.790797696664, 31655.562830303752, 31680.32752211253, 31771.11529968289, 31810.089829518733, 31798.6414451466, 31790.29578597179], [34346.71094688274, 34381.34847608993, 34654.95751852385, 34727.75342170158, 34994.015138749346, 35157.4478049447, 35315.035075943306, 35611.978478203455, 36037.21581238935, 36431.63848320556  …  37134.60998954267, 37297.7364425806, 37450.739438447155, 37434.198725450166, 37500.216248453435, 37581.433828456575, 37692.66465472193, 37732.35226276099, 37737.32727679038, 37728.5972559294], [28335.329312139656, 28363.904560528426, 28589.626386764834, 28649.681507975867, 28869.341942054463, 29004.170526490627, 29134.176780249614, 29379.14897287443, 29729.961017690242, 30055.35159971817  …  30635.288617848295, 30769.864582734706, 30896.08890390267, 30882.443156263194, 30936.90625340115, 31003.909084585863, 31095.67222594645, 31128.413685387422, 31132.51796952319, 31125.315882069168], [978.0617717704338, 979.0481149238879, 986.631816185533, 989.237704949509, 997.8516399498787, 1001.9723552930747, 1008.6595269051724, 1019.3027117218727, 1033.9249414243475, 1048.6019419455813  …  1069.6036233570665, 1076.1831809535954, 1088.0485106838883, 1089.267738703801, 1090.7767096763184, 1091.3678452351917, 1092.8332355861517, 1090.9702281119062, 1094.9941713388519, 1094.335034653094], [0.0019383188997990075, 0.00724378820104743, 0.003748530423508978, -0.0014728174798227522, 0.001987868801486492, 0.0028605161855819183, 0.004248022486102343, 0.0018732557963270224, 0.0009849990897130567, 0.0019701775886113992  …  0.007842429087604907, 0.005384985299174305, 0.008384247664994904, 0.004503877418120084, 0.005173638641051692, -6.181967787088904e-5, -0.0012619167197592818, 0.0011782152412864733, 0.0036568691530483033, 0.0024883168135900746], [2.3548476e6, 2.350053562538022e6, 2.3451011899073543e6, 2.3423915183345713e6, 2.330635046962093e6, 2.3414069624112234e6, 2.3351740057000383e6, 2.367699477685215e6, 2.371212943171501e6, 2.3635432765794625e6  …  2.3739573948865226e6, 2.3840546426363094e6, 2.3748316430023075e6, 2.383492871244243e6, 2.373461373853914e6, 2.367368990120676e6, 2.356431031581976e6, 2.351722693484703e6, 2.343641672690954e6, 2.3259597788663018e6], [0.0016459319014481277, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01  …  0.009364641682891608, 0.009213280859730826, 0.008343077632290594, 0.008187496160241696, 0.0072861037594402645, 0.006492794121726821, 0.0055287011519085, 0.004949900742460868, 0.004329376312192413, 0.0033238723638444384], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.6059091613051 258.503696225116 … 137.13538556670355 505.41380923410753; … ; 532.4229714187146 268.67999258679026 … 146.69046728707514 542.0429385526436; 533.8041163889502 269.23061327380617 … 146.82334966486542 542.6654277436656], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.0834390395163 258.2432663779743 … 136.9972283642248 504.90462950861695; … ; 522.3234395500785 263.58340154308564 … 143.90789566879337 531.7609255179798; 523.150805186002 263.8574859027227 … 143.89314589195288 531.8352683212494]), BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72422.0, 72493.68175199926, 72592.92600456865, 72928.03386641094, 73660.29972234239, 74127.27357364717, 74281.25782631991, 74689.35249335053, 74841.09504564256, 74731.58564433792  …  74645.02980705258, 74501.30047394136, 74239.88814839994, 74350.27333725225, 74416.08084504871, 74499.59655476661, 74504.10393778692, 74614.3555706906, 74711.44807329068, 74878.77603502558], [72422.0, 72420.64790864201, 72446.73202913853, 72707.84155278743, 73363.91137781547, 73754.62714000931, 73833.37875992566, 74164.22063978465, 74240.02768037893, 74056.71401393478  …  73821.97068841838, 73605.59733334939, 73273.43402685449, 73308.45305674979, 73299.41839377131, 73307.75243597664, 73238.32926177968, 73272.81456807676, 73294.24654128727, 73384.39470841936], [64900.92049553802, 64966.370940930196, 65073.459474435644, 65397.93518608807, 66078.47175792385, 66487.01679924561, 66615.3674013941, 66987.45426602564, 67106.59695631843, 66984.92777448626  …  66881.61865961427, 66722.5172216416, 66472.04473073082, 66577.13025598628, 66630.14599960626, 66707.67450171078, 66713.9801950258, 66812.0884136409, 66896.34811783193, 67047.60820592065], [64900.92049553802, 64900.920495538005, 64942.40885753445, 65200.47857178794, 65812.59055832215, 66152.77882583838, 66213.70984426166, 66516.47353249326, 66567.64726030256, 66380.01317204828  …  66144.16130647098, 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Now, apply a shock to the model and simulate it again. A shock is simply a function that takes the model and changes some of its parameters for a specific time period.

In this case, let's define an interest rate shock that sets the interest rate for a number of epochs.

We do this by first defining a "struct" with some useful attributes

struct CustomShock
+model = Bit.init_model(parameters, initial_conditions, T);

Simulate the model for T quarters

data_vec_baseline = Bit.run_n_sims(model, 4)
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0.00308769694208455, 0.005314132363920754], [2.3548476e6, 2.3687648303092e6, 2.3860582276487984e6, 2.384606045619721e6, 2.3987796081209406e6, 2.4341183776553166e6, 2.408110836935148e6, 2.404706232347179e6, 2.3938179446195792e6, 2.378401835262225e6  …  2.3901119380891463e6, 2.4037402064360897e6, 2.4239296514834506e6, 2.4323856674984875e6, 2.415413677701013e6, 2.422410127577235e6, 2.421472767881995e6, 2.414829082215128e6, 2.422191558027239e6, 2.430460331146419e6], [0.0016459319014481277, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01  …  0.009278762755746846, 0.009199445954153649, 0.009433659105414561, 0.009264091109315262, 0.007957986640515696, 0.007680537109078445, 0.007152249430102528, 0.006378469941033385, 0.0062769939884431535, 0.006270014165648097], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086171; 518.6059091613055 258.5036962251161 … 137.13538556670355 505.41380923410765; … ; 526.7390293353282 267.8314086437382 … 145.0687168200554 534.3762620546988; 526.9284096360673 269.4273793649108 … 145.43734171444385 534.6082799092103], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086171; 518.0834390395163 258.2432663779743 … 136.9972283642248 504.90462950861695; … ; 516.7473161696616 262.7509143896827 … 142.3169081879774 524.2396782140644; 516.4123192628743 264.0503250656925 … 142.5347989759295 523.9389197400261])])

Now, apply a shock to the model and simulate it again. A shock is simply a function that takes the model and changes some of its parameters for a specific time period.

In this case, let's define an interest rate shock that sets the interest rate for a number of epochs.

We do this by first defining a "struct" with some useful attributes

struct CustomShock
     rate::Float64    # target rate for the first 10 epochs
     final_time::Int  # number of epochs for the shock
 end

and then by making the struct a callable function that changes the interest rate in the model, this is done in Julia using the syntax below

function (s::CustomShock)(model::Bit.Model)
@@ -32,7 +32,7 @@
         model.cb.r_bar = s.rate
     end
 end

Now we define a specific shock with a rate of 0.01 for the first 10 epochs, and run a shocked simulation

custom_shock = CustomShock(0.0, 10)
-data_vec_shocked = Bit.run_n_sims(model, 4; shock = custom_shock)
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41591.880136263775, 41559.15106294764, 41701.89529516255], [14866.888022051655, 14678.310283044393, 14548.103724034225, 14583.703915522454, 14853.84383759662, 14997.968819243095, 15247.34989426102, 15425.556100495993, 15494.819697158542, 15353.056184558756  …  15610.563850281156, 15495.948810534806, 15316.128511012761, 15274.373045439179, 15169.494011999817, 15251.850446949851, 15437.944533132133, 15686.732192758185, 15748.692045844426, 15925.582435397044], [14866.888022051655, 14663.52260240718, 14518.805481967895, 14539.67119811449, 14794.076144531526, 14922.572284029242, 15155.415952436935, 15317.100870976392, 15370.376963591832, 15214.4087576427  …  15438.436959084125, 15309.646424834798, 15116.743303211813, 15060.343548351566, 14941.865733599248, 15007.85143089477, 15175.66422631894, 15404.690027109891, 15449.955090933197, 15607.750143431474], [15944.236265986641, 16125.288136761677, 16225.875598021223, 16344.881739091943, 15868.970558037032, 15666.552065365862, 15973.216504474605, 16165.037414081226, 16124.939209388307, 16507.02608518702  …  16495.060801564672, 16645.266039107333, 16919.68430649799, 16847.512760722788, 17012.2865466317, 16887.927873380195, 16832.236481640946, 16965.374595490124, 17160.50574461752, 17267.095787192324], [15944.236265986641, 16109.042696615545, 16193.198512400524, 16295.53148055318, 15805.118280340981, 15587.794484341925, 15876.905947751655, 16051.382980392404, 15995.435829998785, 16357.957608837065  …  16313.181174210393, 16445.14581338816, 16699.424025386357, 16611.439916840573, 16757.005948926617, 16617.754900032032, 16546.26809127122, 16660.34286975214, 16834.988094265318, 16922.49045474091], [15944.236265986641, 15950.991887418772, 15965.890886310965, 15980.682330211914, 16008.604734925673, 16030.954027846396, 16084.512965805654, 16168.848586162077, 16257.846212125569, 16324.6626240632  …  16528.15821294373, 16622.02989187365, 16731.05698136548, 16802.26400781346, 16882.67323823581, 16951.013372632628, 17008.622335729266, 17094.54929790693, 17173.90636104979, 17270.110811478695], [15944.236265986641, 15934.922042230224, 15933.737380612552, 15932.431702448997, 15944.190608544734, 15950.364555735918, 15987.531346735988, 16055.16735658077, 16127.275423688761, 16177.241000583732  …  16345.913643324586, 16422.18902624511, 16513.25224888622, 16566.8252042156, 16629.3375738439, 16679.83115782891, 16719.657268189952, 16787.195054491654, 16848.134514391302, 16925.445307157075], [3173.2320350842083, 3167.1085184603785, 3169.855097308701, 3169.598643703468, 3181.309754584695, 3174.9348221335454, 3149.7515817170515, 3142.986919477989, 3166.124543167406, 3135.7440361173158  …  3157.6278446920414, 3186.2350105491764, 3199.1014507605414, 3224.813150310475, 3252.201157192083, 3263.4323959134153, 3262.522751410444, 3278.8195669096085, 3284.8249197515574, 3299.4055971651055], [3173.2320350842083, 3163.9178113277967, 3163.471366224716, 3160.0286440529467, 3168.5090582103458, 3158.974055178784, 3130.760145127612, 3120.8889564931656, 3140.696613016231, 3107.4263619781227  …  3122.8108662871105, 3147.928018758996, 3157.455580064329, 3179.6260166252346, 3203.3997304709023, 3211.224023143513, 3207.094681540876, 3219.867494543121, 3222.515072617171, 3233.5582319384353], [34195.564496956766, 34473.73634720634, 33307.56769074658, 31534.498987374245, 31950.177616274326, 32879.223936336326, 32070.96431049245, 31512.08739406152, 32144.486720507437, 32689.84373477279  …  31578.04654851721, 33081.438843550524, 33730.22294521053, 34634.57367109981, 36084.633423153784, 35708.49083684909, 37213.2599086452, 35861.44903776601, 34934.40884770468, 34546.29623418328], [34195.564496956766, 34439.00574173175, 33240.49000149199, 31439.286571477987, 31821.61908085256, 32713.93625003713, 31877.592335191184, 31290.529697943544, 31886.3263885526, 32394.63458125765  …  31229.857268834425, 32683.71224711571, 33291.1232401991, 34149.262728214155, 35543.15966164725, 35137.22660505581, 36581.03162168569, 35216.67225279821, 34271.73801190402, 33856.84399244753], [33097.63671130043, 33287.3278304154, 32060.601777784013, 30454.115672028096, 30817.227127249527, 31507.38708492891, 30695.758991312316, 30177.810134443727, 30581.709156854344, 30883.209809053664  …  29521.604315381737, 30726.55479115804, 31207.394828518554, 32079.569435104248, 33352.699141696605, 32807.3958184391, 34268.62966367631, 32955.05775303278, 31934.14157956166, 31573.31125396772], [33097.63671130043, 33253.79247357048, 31996.03533740827, 30362.16526786031, 30693.22726619019, 31348.995782181475, 30510.678820599158, 29965.633581248057, 30336.099878463127, 30604.31565772239  …  29196.09000195328, 30357.140144018602, 30801.1372747029, 31630.06004492783, 32852.21985876001, 32282.543288126646, 33686.42866645372, 32362.536905254125, 31328.382816522062, 30943.1918896369], [29576.14777688488, 29605.974349916614, 29656.62324050609, 29682.75110255367, 29688.660970461802, 29735.540164066962, 29895.029745278607, 30077.79921361204, 30216.273911172513, 30438.14312063819  …  30860.720688755286, 30997.18277240912, 31254.01204513351, 31382.93630438581, 31525.779036106625, 31686.41086236949, 31813.695759109854, 31982.804710905904, 32114.61610587396, 32282.941305211272], [34346.71094688274, 34381.34847608993, 34368.85327084062, 34326.97027225442, 34312.7372623672, 34458.33186985679, 34771.52081208963, 35019.08826194116, 35167.02871461891, 35454.337358678655  …  35866.737076448706, 35999.84480174523, 36240.40259794807, 36310.442279934505, 36437.034296536054, 36580.17594977118, 36735.27635116767, 36992.07228222386, 37205.15369031167, 37440.026047671265], [28335.329312139656, 28363.904560528426, 28353.596273481395, 28319.043662043423, 28307.30172195254, 28427.414275252602, 28685.78870384963, 28890.026752417318, 29012.074579695272, 29249.09829519011  …  29589.319570796943, 29699.1307034, 29897.58593204042, 29955.367227520863, 30059.803034612843, 30177.891951160753, 30305.846588704095, 30517.697944245756, 30693.48571844632, 30887.250577107603], [978.0617717704338, 979.0481149238879, 978.8594170008136, 971.1009761236772, 962.8487754257769, 966.0590010786302, 976.8114226899794, 981.2240255768843, 982.2161334536762, 990.4196167145631  …  997.008691104916, 996.5839672254109, 1005.7663463774339, 1009.8930991230985, 1014.929450631975, 1023.7777320072493, 1026.509172773949, 1039.3301158892873, 1042.1202016216066, 1044.0631571809167], [0.0019383188997990075, 0.0031116166842983795, 0.005001415834238632, -0.0006046845000774459, 0.0007162998311414093, 0.003847503148789455, 0.004383817340697371, 0.0014135740127474428, 0.004783827521142259, 0.006915480608194358  …  0.004857630533876778, 0.0014513808843388265, 0.0030726587120351034, 0.004936660536231985, 0.004350381175569185, 0.002992886948375295, 0.002906999053189141, 0.010047624903340768, 0.008739102392750775, 0.007300502193050784], [2.3548476e6, 2.377714866943316e6, 2.3525037396194185e6, 2.323800353345539e6, 2.3348806057195384e6, 2.341334411127313e6, 2.327291336613708e6, 2.314826895652856e6, 2.3304031389127e6, 2.3291108368047527e6  …  2.2993394195536976e6, 2.351551810700935e6, 2.362484221074015e6, 2.3664950447712075e6, 2.3908469640023992e6, 2.366980008303181e6, 2.3911223670204342e6, 2.377806147265157e6, 2.360922120628925e6, 2.339989427512156e6], [0.0016459319014481277, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0022668203900027026, 0.0026267892492102805, 0.0027125935131079876, 0.0036237565017843364, 0.0024422679309108247, 0.003331405519297616, 0.002767575476358736, 0.00206480660788685, 0.0012073839982784367], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.6059091613051 258.5036962251161 … 137.13538556670355 505.41380923410753; … ; 579.054527741374 288.85573366414025 … 146.52306728008196 533.0374938450894; 587.7040703387366 292.08572839171796 … 147.00084613538945 535.4678976809846], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.90462950861706; 518.0834390395163 258.2432663779743 … 136.9972283642248 504.90462950861695; … ; 568.0704418349717 283.3764289680881 … 143.74367107269634 522.9263050999383; 575.9750593320128 286.25647367643455 … 144.06710000474615 524.7813818261134])])

Finally, we can plot baseline and shocked simulations

Te = T + 1
+data_vec_shocked = Bit.run_n_sims(model, 4; shock = custom_shock)
BeforeIT.DataVector(BeforeIT.Data[BeforeIT.Data{Float64, Vector{Float64}, Matrix{Float64}}([72421.99999999999, 72494.62771835191, 72460.21210281359, 72338.27360848119, 72245.69191602505, 72564.24731469502, 72915.22233910079, 73143.95721498068, 73275.82614340048, 73521.93422729315  …  73790.36220090448, 74198.76844420111, 74245.62862328584, 74594.807622292, 74727.57319238705, 75187.25377612305, 75662.05858251023, 76013.87267697023, 76302.6240153369, 76961.40870142875], [72421.99999999999, 72421.59292197971, 72314.2853982315, 72119.86196367319, 71954.99555574806, 72199.45839601758, 72475.57978776557, 72629.690849733, 72687.32984025749, 72857.98648425315  …  72976.72691086873, 73306.702540643, 73279.09977229555, 73549.56084762882, 73606.23659267034, 73984.40852104583, 74376.61103497229, 74647.16882777569, 74855.2394653231, 75425.46356286289], [64900.92049553802, 64966.370940930254, 64932.06215826303, 64814.06727390888, 64734.33115945278, 65015.83190711432, 65341.869251984725, 65551.85545130525, 65666.5511325071, 65889.48960824721  …  66116.90800722614, 66490.08006220536, 66526.68462431747, 66849.45135630805, 66964.08043131496, 67398.67750532355, 67847.12540057099, 68152.78603538385, 68452.21165721263, 69064.01544926113], [64900.92049553802, 64900.920495538005, 64801.29629962835, 64618.373537608095, 64473.85840386077, 64688.989751424946, 64947.890255755985, 65090.96824036176, 65139.1668010633, 65294.4674781138  …  65387.88258956712, 65690.69302934718, 65660.64090911929, 65912.73503991257, 65959.24016893031, 66320.43384879697, 66694.44831792783, 66927.42187845265, 67153.74158694338, 67685.68128712723], [40512.94792630534, 40553.094078064045, 40588.62678280894, 40575.335254134174, 40503.89075444192, 40693.96379203388, 40785.22974675203, 40879.296421057035, 40984.51609735252, 41076.525280401955  …  41167.59708820362, 41288.71654053777, 41391.783421829656, 41525.44488504152, 41607.640241500165, 41644.53163054268, 41735.23425588173, 42020.261221106186, 41885.16453635439, 42122.22327526685], [40512.94792630534, 40512.2388166264, 40506.88586074926, 40452.82575439887, 40340.91448129527, 40489.3904987423, 40539.31508192018, 40591.87900498058, 40655.359300645854, 40705.57929620711  …  40713.67046692295, 40792.31670803667, 40852.945599146165, 40943.57680714099, 40983.29011426502, 40978.3026521461, 41026.1806625059, 41264.751065754725, 41090.64480905746, 41281.57566814957], [14866.888022051655, 14784.100223154117, 14712.886813686951, 14433.39975251384, 14571.043860552141, 14901.58372822992, 14848.612884518387, 14892.351870524324, 14998.756050728727, 15189.736929281695  …  15713.765147519944, 15767.54828394104, 15855.731942763698, 15932.582037888054, 16111.422574354292, 16387.227014146836, 16423.432337480528, 16562.022297318046, 16646.537695650037, 16783.015775792865], [14866.888022051655, 14769.20596431964, 14683.25671704077, 14389.820849909958, 14512.414075116312, 14826.67173208908, 14759.083128687966, 14787.645535810527, 14878.2973271338, 15052.564374484766  …  15540.500326993031, 15577.980552538762, 15649.322182884012, 15709.329501713117, 15869.660035651019, 16125.064250275555, 16144.409254896189, 16264.242710157918, 16330.769505724313, 16448.07139358557], [15944.236265986641, 16183.74556412745, 16307.19297558443, 16126.758751698408, 15778.9650092627, 15646.734822779852, 15825.332937355839, 16229.341625926638, 16059.728889980654, 16282.146300010148  …  16254.735606294478, 16468.465860172986, 15675.244696873831, 16576.18407729156, 15924.545318429824, 16335.892363466925, 16529.12297062043, 16260.355862558115, 16469.767139361866, 16688.092570300007], [15944.236265986641, 16167.441230978644, 16274.352125925687, 16078.067073992046, 15715.474888599678, 15568.076865341389, 15729.91404504341, 16115.235077058947, 15930.74923081261, 16135.108624772714  …  16075.50587865778, 16270.471241315945, 15471.184518156333, 16343.91317941965, 15685.586996400147, 16074.550851043068, 16248.30427517923, 15968.00013637283, 16157.352110292277, 16355.042602949114], [15944.236265986641, 15960.26748517238, 15956.360572450481, 15933.835647332611, 15910.221438679142, 15960.513368728336, 16014.705337042968, 16082.752021618435, 16098.46857911013, 16134.612444518953  …  16174.340583489206, 16214.220528156664, 16253.261517654006, 16311.04079203814, 16341.01080256013, 16393.34137525488, 16465.38461636674, 16571.20198093318, 16622.89218867258, 16729.134095244113], [15944.236265986641, 15944.18829527217, 15924.22625973062, 15885.726464209602, 15846.203179032573, 15880.278010011469, 15918.144623279817, 15969.676126626482, 15969.177791906586, 15988.907089648288  …  15995.997316167195, 16019.282612215131, 16041.676721743539, 16082.485168359815, 16095.80314082994, 16131.08079386963, 16185.648792746564, 16273.257346150962, 16307.57253039197, 16395.265048152076], [3173.2320350842083, 3176.3841162139906, 3179.1596849869643, 3175.854737805204, 3168.4488688684714, 3184.4303091918896, 3183.1732588382847, 3197.2946124235914, 3196.9928440567946, 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3164.0882343712697, 3182.998582579784, 3211.942187668377  …  3199.640505176152, 3192.3838456703074, 3198.039576402093, 3198.3346580745124, 3209.908841853045, 3227.0956779905055, 3219.6583370071394, 3234.9129521918103, 3269.9154085110763, 3279.8496302357225], [34195.564496956766, 33941.31696122601, 34541.48120778274, 33714.853104280075, 34535.21995491239, 34390.16906265721, 35026.148928995804, 34810.506853681385, 33715.741972836346, 34048.57804500361  …  35603.9928735862, 36224.927487915156, 35684.49344755134, 36308.95928132333, 35598.68041750616, 37268.214095124924, 38046.94318228579, 39181.189640748635, 39173.270927745434, 40147.59013835396], [34195.564496956766, 33907.122742276384, 34471.918555704375, 33613.05752424152, 34396.25993562257, 34217.28567931425, 34814.95865919207, 34565.75836708459, 33444.962500901034, 33741.098695006935  …  35211.412268150736, 35789.40782433556, 35219.95306868222, 35800.186301140086, 35064.49870186279, 36671.99741958293, 37400.55117655325, 38476.72503695697, 38430.19311297798, 39346.35095967179], [33097.63671130043, 32789.113983054056, 33115.22447286899, 32111.24798408362, 32972.13134608254, 32739.170132308715, 33223.41509082889, 33300.5359747549, 32362.41430620633, 32447.130443721755  …  33869.46700272432, 34608.24109476071, 34065.32711583378, 34527.623207120065, 33738.33125183263, 35352.234800179096, 36195.34363225388, 37107.37363417192, 37295.65128815642, 38402.30360774021], [33097.63671130043, 32756.08055232472, 33048.5341411879, 32014.294184398022, 32839.46075606286, 32574.586512786467, 33023.094410098114, 33066.403911784895, 32102.50374385102, 32154.11313871401  …  33496.011814456106, 34192.158287591534, 33621.86502807993, 34043.81088901909, 33232.065304340205, 34786.66995041883, 35580.40905902057, 36440.19554426115, 36588.190042179536, 37635.895708881675], [29576.147776884878, 29605.974349916596, 29644.80589823132, 29864.438113513675, 30056.12149721427, 30216.788875313658, 30442.697938084053, 30661.54214339098, 30744.091329035793, 30751.44656417656  …  30902.704504797548, 31037.951248173013, 31152.17249822122, 31248.973175563366, 31381.84685923034, 31499.72035602876, 31626.630608239193, 31727.455016805416, 31820.922986563495, 31835.335610683655], [34346.71094688275, 34381.34847608993, 34406.62679751473, 34741.70214695546, 34937.363914673835, 35172.48390076224, 35540.83372166018, 35744.576657912956, 35888.19535603899, 35845.831852382165  …  35998.126563339945, 36268.23666550518, 36394.47025574865, 36550.30117375795, 36677.611935461944, 36758.1334037712, 36923.29362449906, 37060.75039740559, 37087.5904516608, 37107.03696958948], [28335.329312139664, 28363.90456052843, 28384.75865520834, 28661.188919097844, 28822.605848674288, 29016.574995938023, 29320.45601363873, 29488.539740860033, 29607.022209620292, 29572.073191392945  …  29697.71319209518, 29920.548464655167, 30024.688577381945, 30153.24587608909, 30258.27463309121, 30324.703186461807, 30460.95696781593, 30574.35597514778, 30596.498467255777, 30612.541444131657], [978.0617717704338, 979.048114923888, 979.4380968724001, 988.0019737044403, 987.6674986138592, 994.7704510353591, 1005.1829490717198, 1012.7427213553946, 1016.5622742548561, 1015.5314808490373  …  1018.0890111954292, 1029.2139775836754, 1036.5861081612104, 1039.537366797521, 1044.085913021053, 1041.6065164699642, 1051.2250139977004, 1060.4460352795556, 1066.6953128539508, 1071.3343427909754], [0.0019383188997990075, 0.0021482922905946555, 0.0029712923943614467, -0.002690603341409692, -0.0023109660720772585, -0.0020991168317761044, 0.0024849395657342566, 0.005246064555408436, 0.004875284619014764, 0.006608008976924262  …  0.0038332051479208484, 0.006075554538786276, 0.004727648308306298, 0.008233624616221569, 0.005033825511806089, 0.003473288176790046, 0.002264390627182866, 0.003793032400694285, 0.002395137024686411, -0.0013880937638373059], [2.3548476e6, 2.3688307598037766e6, 2.390378160488792e6, 2.3790295826195325e6, 2.396770381101069e6, 2.3856050941130305e6, 2.3903544397639646e6, 2.378072376864198e6, 2.356238491086789e6, 2.368247001663375e6  …  2.385157208815031e6, 2.3879161831665738e6, 2.389805116477663e6, 2.4200977101261243e6, 2.409506580047036e6, 2.434654438267247e6, 2.4459577660503387e6, 2.4653323119519423e6, 2.461171828485392e6, 2.4783518479842e6], [0.0016459319014481277, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.00048792312266989353, 0.0007049981978050916, 0.0008391016753290276, 0.0022127125486990826, 0.0017339600489103364, 0.002711870605259883, 0.003019719531273557, 0.0036623164153140467, 0.0032801555527221804, 0.0036920551568293126], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086171; 518.6059091613055 258.503696225116 … 137.13538556670355 505.41380923410765; … ; 541.3702914166838 277.16688185065715 … 145.89678423685416 533.0058975125235; 537.1859459572509 277.05587600027644 … 146.47332745833953 534.0247732857426], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086171; 518.0834390395163 258.2432663779743 … 136.9972283642248 504.90462950861695; … ; 531.1010378261951 271.9093029961526 … 143.12926799313058 522.8953081182322; 526.4651424257039 271.52657718631195 … 143.5501091982366 523.3670583576766])])

Finally, we can plot baseline and shocked simulations

Te = T + 1
 StatsPlots.errorline(
     1:Te,
     data_vec_baseline.real_gdp,
@@ -50,4 +50,4 @@
     titlefont = 10,
     xlabel = "quarters",
     ylabel = "GDP",
-)
Example block output

Note that, importantly, once the function central_bank_rate has been changed, the model will use the new interest rate in all the simulations, unless the function is changed again. To restore the original interest rate, we can simply re-import the function central_bank_rate.

+)Example block output

Note that, importantly, once the function central_bank_rate has been changed, the model will use the new interest rate in all the simulations, unless the function is changed again. To restore the original interest rate, we can simply re-import the function central_bank_rate.

diff --git a/previews/PR39/index.html b/previews/PR39/index.html index d91976c..ec80da6 100644 --- a/previews/PR39/index.html +++ b/previews/PR39/index.html @@ -289,4 +289,4 @@ [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.52.0+1 [3f19e933] p7zip_jll v17.4.0+2 -Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

You can also download the manifest file and the project file.

+Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

You can also download the manifest file and the project file.