Update scenario_analysis_via_shock.jl

examples/scenario_analysis_via_shock.jl

@@ -1,42 +1,45 @@
 # # Scenario analysis via custom shocks
 
-# In this tutorial we will illustrate how to perform a scenario analysis by running the model multiple times
-# under a specific shock and comparing the results with the unshocked model.
+# In this tutorial we will illustrate how to perform a scenario analysis by
+# running the model multiple times under a specific shock and comparing the
+# results with the unshocked model.
 
 import BeforeIT as Bit
 using Plots, StatsPlots
 
-
 parameters = Bit.AUSTRIA2010Q1.parameters
 initial_conditions = Bit.AUSTRIA2010Q1.initial_conditions
 
-# initialise the model and the data collector
+# 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)
 
-# 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.
+# 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.
+# 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
+# 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)
     if model.agg.t <= s.final_time
         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
+# 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)
@@ -63,6 +66,7 @@ StatsPlots.errorline!(
     ylabel = "GDP",
 )
 
-# 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
+# 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`.