Cleaner tests

test/accounting_identities.jl

@@ -1,53 +1,43 @@
 using BeforeIT, MAT, FileIO, Random
 using Test
 
-dir = @__DIR__
-
-parameters = BeforeIT.AUSTRIA2010Q1.parameters
-initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
-
-T = 1
-model = BeforeIT.initialise_model(parameters, initial_conditions, T)
-data = BeforeIT.initialise_data(model)
-
-println(BeforeIT.get_accounting_identities(data))
-println(BeforeIT.get_accounting_identity_banks(model))
-
-for t in 1:T
-    println(t)
-    BeforeIT.one_epoch!(model; multi_threading = false)
-    BeforeIT.update_data!(data, model)
-end
-
-# println(BeforeIT.get_accounting_identities(data))
-# println(BeforeIT.get_accounting_identity_banks(model))
-
-# income accounting and production accounting should be equal
-zero = sum(data.nominal_gva - data.compensation_employees - data.operating_surplus - data.taxes_production)
-# println(zero)
-@test isapprox(zero, 0.0, atol = 1e-9)
-
-# compare nominal_gdp to total expenditure
-zero = sum(
-    data.nominal_gdp - data.nominal_household_consumption - data.nominal_government_consumption -
-    data.nominal_capitalformation - data.nominal_exports + data.nominal_imports,
-)
-# println(zero)
-@test isapprox(zero, 0.0, atol = 1e-9)
-
-zero = sum(
-    data.real_gdp - data.real_household_consumption - data.real_government_consumption - data.real_capitalformation - data.real_exports + data.real_imports,
-)
-# println(zero)
-@test isapprox(zero, 0.0, atol = 1e-8)
-
-# accounting identity of balance sheet of central bank
-zero = model.cb.E_CB + model.rotw.D_RoW - model.gov.L_G + model.bank.D_k
-# println(zero)
-@test isapprox(zero, 0.0, atol = 1e-9)
-
-# accounting identity of balance sheet of commercial bank
-tot_D_h = sum(model.w_act.D_h) + sum(model.w_inact.D_h) + sum(model.firms.D_h) + model.bank.D_h
-zero = sum(model.firms.D_i) + tot_D_h + sum(model.bank.E_k) - sum(model.firms.L_i) - model.bank.D_k
-# println(zero)
-@test isapprox(zero, 0.0, atol = 1e-9)
+@testset "accounting identities" begin
+    dir = @__DIR__
+
+    parameters = BeforeIT.AUSTRIA2010Q1.parameters
+    initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
+
+    T = 1
+    model = BeforeIT.initialise_model(parameters, initial_conditions, T)
+    data = BeforeIT.initialise_data(model)
+
+    for t in 1:T
+        BeforeIT.one_epoch!(model; multi_threading = false)
+        BeforeIT.update_data!(data, model)
+    end
+
+    # income accounting and production accounting should be equal
+    zero = sum(data.nominal_gva - data.compensation_employees - data.operating_surplus - data.taxes_production)
+    @test isapprox(zero, 0.0, atol = 1e-8)
+
+    # compare nominal_gdp to total expenditure
+    zero = sum(
+        data.nominal_gdp - data.nominal_household_consumption - data.nominal_government_consumption -
+        data.nominal_capitalformation - data.nominal_exports + data.nominal_imports,
+    )
+    @test isapprox(zero, 0.0, atol = 1e-8)
+
+    zero = sum(
+        data.real_gdp - data.real_household_consumption - data.real_government_consumption - data.real_capitalformation - data.real_exports + data.real_imports,
+    )
+    @test isapprox(zero, 0.0, atol = 1e-8)
+
+    # accounting identity of balance sheet of central bank
+    zero = model.cb.E_CB + model.rotw.D_RoW - model.gov.L_G + model.bank.D_k
+    @test isapprox(zero, 0.0, atol = 1e-8)
+
+    # accounting identity of balance sheet of commercial bank
+    tot_D_h = sum(model.w_act.D_h) + sum(model.w_inact.D_h) + sum(model.firms.D_h) + model.bank.D_h
+    zero = sum(model.firms.D_i) + tot_D_h + sum(model.bank.E_k) - sum(model.firms.L_i) - model.bank.D_k
+    @test isapprox(zero, 0.0, atol = 1e-8)
+end

test/deterministic/deterministic_ouput_t1_t5.jl

@@ -1,7 +1,6 @@
 @testset "time 1 and 5 deterministic" begin
 
     dir = @__DIR__
-
     T = 1
     parameters = BeforeIT.AUSTRIA2010Q1.parameters
     initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
@@ -28,30 +27,29 @@
         end
     end
 
+    T = 5
+    parameters = BeforeIT.AUSTRIA2010Q1.parameters
+    initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
+    model = BeforeIT.initialise_model(parameters, initial_conditions, T)
+    data = BeforeIT.initialise_data(model)
+    for t in 1:T
+        BeforeIT.one_epoch!(model; multi_threading = false)
+        BeforeIT.update_data!(data, model)
+    end
 
-    # T = 4
-    # parameters = matread(joinpath(dir, "../../data/austria/parameters/2010Q1.mat"))
-    # initial_conditions = matread(joinpath(dir, "../../data/austria/initial_conditions/2010Q1.mat"))
-    # model = BeforeIT.initialise_model(parameters, initial_conditions, T)
-    # data = BeforeIT.initialise_data(model)
-    # for t in 1:T-1
-    #     BeforeIT.one_epoch!(model; multi_threading = false)
-    #     BeforeIT.update_data!(data, model)
-    # end
-
-    # output_t5 = matread(joinpath(dir, "../matlab_code/output_t5.mat"))list
+    output_t5 = matread(joinpath(dir, "../matlab_code/output_t5.mat"))
 
-    # # confront results between julia and matlab code
+    # confront results between julia and matlab code
 
-    # for fieldname in fieldnames(typeof(data))
-    #     julia_output = getfield(data, fieldname)
-    #     matlab_output = output_t5[string(fieldname)]
+    for fieldname in fieldnames(typeof(data))
+        julia_output = getfield(data, fieldname)
+        matlab_output = output_t5[string(fieldname)]
 
-    #     if length(size(julia_output)) == 1
-    #         @test isapprox(julia_output, matlab_output', rtol = 1e-4)
-    #     else
-    #         @test isapprox(julia_output, matlab_output, rtol = 1e-5)
-    #     end
-    # end
+        if length(julia_output) == 6
+            @test isapprox(julia_output[2:end], matlab_output', rtol = 1e-4)
+        else
+            @test isapprox(julia_output[2:end, :], matlab_output, rtol = 1e-5)
+        end
+    end
 
 end

test/utils/epsilon.jl → test/deterministic/epsilon.jl

@@ -2,6 +2,7 @@ using BeforeIT
 using Test
 import Random: randn
 
+
 function randn(n1::Int, n2::Int)
     return ones(n1, n2)
 end

test/deterministic/runtests_deterministic.jl

@@ -1,4 +1,5 @@
 @warn "making the model deterministic all subsequent calls to the model will be deterministic."
+include("epsilon.jl")
 include("make_model_deterministic.jl")
 include("initialize_deterministic.jl")
 include("one_epoch_deterministic.jl")

test/markets/search_and_matching.jl

@@ -1,102 +1,104 @@
 using BeforeIT, Test, MAT, StatsBase
 using Random
 
-Random.seed!(1)
-
-dir = @__DIR__
-
-parameters = BeforeIT.AUSTRIA2010Q1.parameters
-initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
-
-T = 1
-model = BeforeIT.initialise_model(parameters, initial_conditions, T;)
-
-
-gov = model.gov # government
-cb = model.cb # central bank
-rotw = model.rotw # rest of the world
-firms = model.firms # firms
-bank = model.bank # bank
-w_act = model.w_act # active workers
-w_inact = model.w_inact # inactive workers
-agg = model.agg # aggregate variables
-
-prop = model.prop # model properties
-
-
-gamma_e = 0.01 # set expected growth in euro area
-pi_e = 0.001   # set expected inflation in euro area
-
-agg.gamma_e = gamma_e
-agg.pi_e = pi_e
-
-Q_s_i, I_d_i, DM_d_i, N_d_i, Pi_e_i, DL_d_i, K_e_i, L_e_i, P_i =
-    BeforeIT.firms_expectations_and_decisions(model.firms, model)
-
-firms.Q_s_i .= Q_s_i
-firms.I_d_i .= I_d_i
-firms.DM_d_i .= DM_d_i
-firms.N_d_i .= N_d_i
-firms.Pi_e_i .= Pi_e_i
-firms.P_i .= P_i
-firms.DL_d_i .= DL_d_i
-firms.K_e_i .= K_e_i
-firms.L_e_i .= L_e_i
-
-Pi_e_k = BeforeIT.bank_expected_profits(bank, model)
-bank.Pi_e_k = Pi_e_k
-
-C_d_h, I_d_h = BeforeIT.households_budget_act(w_act, model)
-w_act.C_d_h .= C_d_h
-w_act.I_d_h .= I_d_h
-C_d_h, I_d_h = BeforeIT.households_budget_inact(w_inact, model)
-w_inact.C_d_h .= C_d_h
-w_inact.I_d_h .= I_d_h
-C_d_h, I_d_h = BeforeIT.households_budget(firms, model)
-firms.C_d_h .= C_d_h
-firms.I_d_h .= I_d_h
-bank.C_d_h, bank.I_d_h = BeforeIT.households_budget(bank, model)
-
-C_G, C_d_j = BeforeIT.gov_expenditure(gov, model)
-gov.C_G = C_G
-gov.C_d_j .= C_d_j
-
-
-epsilon_E = 0.28
-epsilon_I = 0.36
-
-agg.epsilon_E = epsilon_E
-agg.epsilon_I = epsilon_I
-
-C_E, Y_I, C_d_l, Y_m, P_m = BeforeIT.rotw_import_export(rotw, model)
-rotw.C_E = C_E
-rotw.Y_I = Y_I
-rotw.C_d_l .= C_d_l
-rotw.Y_m .= Y_m
-rotw.P_m .= P_m
-
-BeforeIT.search_and_matching!(model, false)
-
-rtol = 0.0001
-
-# NOTE: the expected numbers come out of the original implementation, 
-# and only hold for the serial code (without multithreading)
-@test isapprox(mean(w_act.C_h), 4.148850396106796, rtol = rtol)
-@test isapprox(mean(w_inact.C_h), 2.205381003981018, rtol = rtol)
-@test isapprox(mean(firms.C_h), 9.060799641122962, rtol = rtol)
-@test isapprox(bank.C_h, 2931.5395701704915, rtol = rtol)
-
-@test isapprox(mean(w_act.I_h), 0.34186063655926524, rtol = rtol)
-@test isapprox(mean(w_inact.I_h), 0.18217582636296747, rtol = rtol)
-@test isapprox(mean(firms.I_h), 0.7442975169996757, rtol = rtol)
-@test isapprox(bank.I_h, 233.5381841004737, rtol = rtol)
-
-@test isapprox(gov.C_j, 14686.094833493271, rtol = rtol)
-@test isapprox(rotw.C_l, 44241.742486622454, rtol = rtol)
-
-@test isapprox(mean(firms.I_i), 20.671016463479898, rtol = rtol)
-@test isapprox(mean(firms.DM_i), 110.18635469222951, rtol = rtol)
-@test isapprox(mean(firms.P_bar_i), 1.0010000000000023, rtol = rtol)
-@test isapprox(mean(firms.P_CF_i), 1.0010000000000023, rtol = rtol)
-@test isapprox(mean(firms.Q_d_i), 216.70740037345882, rtol = rtol)
-@test isapprox(mean(rotw.Q_d_m), 719.2385742449192, rtol = rtol)
+@testset "search and matching" begin
+    Random.seed!(1)
+
+    dir = @__DIR__
+
+    parameters = BeforeIT.AUSTRIA2010Q1.parameters
+    initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
+
+    T = 1
+    model = BeforeIT.initialise_model(parameters, initial_conditions, T;)
+
+
+    gov = model.gov # government
+    cb = model.cb # central bank
+    rotw = model.rotw # rest of the world
+    firms = model.firms # firms
+    bank = model.bank # bank
+    w_act = model.w_act # active workers
+    w_inact = model.w_inact # inactive workers
+    agg = model.agg # aggregate variables
+
+    prop = model.prop # model properties
+
+
+    gamma_e = 0.01 # set expected growth in euro area
+    pi_e = 0.001   # set expected inflation in euro area
+
+    agg.gamma_e = gamma_e
+    agg.pi_e = pi_e
+
+    Q_s_i, I_d_i, DM_d_i, N_d_i, Pi_e_i, DL_d_i, K_e_i, L_e_i, P_i =
+        BeforeIT.firms_expectations_and_decisions(model.firms, model)
+
+    firms.Q_s_i .= Q_s_i
+    firms.I_d_i .= I_d_i
+    firms.DM_d_i .= DM_d_i
+    firms.N_d_i .= N_d_i
+    firms.Pi_e_i .= Pi_e_i
+    firms.P_i .= P_i
+    firms.DL_d_i .= DL_d_i
+    firms.K_e_i .= K_e_i
+    firms.L_e_i .= L_e_i
+
+    Pi_e_k = BeforeIT.bank_expected_profits(bank, model)
+    bank.Pi_e_k = Pi_e_k
+
+    C_d_h, I_d_h = BeforeIT.households_budget_act(w_act, model)
+    w_act.C_d_h .= C_d_h
+    w_act.I_d_h .= I_d_h
+    C_d_h, I_d_h = BeforeIT.households_budget_inact(w_inact, model)
+    w_inact.C_d_h .= C_d_h
+    w_inact.I_d_h .= I_d_h
+    C_d_h, I_d_h = BeforeIT.households_budget(firms, model)
+    firms.C_d_h .= C_d_h
+    firms.I_d_h .= I_d_h
+    bank.C_d_h, bank.I_d_h = BeforeIT.households_budget(bank, model)
+
+    C_G, C_d_j = BeforeIT.gov_expenditure(gov, model)
+    gov.C_G = C_G
+    gov.C_d_j .= C_d_j
+
+
+    epsilon_E = 0.28
+    epsilon_I = 0.36
+
+    agg.epsilon_E = epsilon_E
+    agg.epsilon_I = epsilon_I
+
+    C_E, Y_I, C_d_l, Y_m, P_m = BeforeIT.rotw_import_export(rotw, model)
+    rotw.C_E = C_E
+    rotw.Y_I = Y_I
+    rotw.C_d_l .= C_d_l
+    rotw.Y_m .= Y_m
+    rotw.P_m .= P_m
+
+    BeforeIT.search_and_matching!(model, false)
+
+    rtol = 0.0001
+
+    # NOTE: the expected numbers come out of the original implementation, 
+    # and only hold for the serial code (without multithreading)
+    @test isapprox(mean(w_act.C_h), 4.148850396106796, rtol = rtol)
+    @test isapprox(mean(w_inact.C_h), 2.205381003981018, rtol = rtol)
+    @test isapprox(mean(firms.C_h), 9.060799641122962, rtol = rtol)
+    @test isapprox(bank.C_h, 2931.5395701704915, rtol = rtol)
+
+    @test isapprox(mean(w_act.I_h), 0.34186063655926524, rtol = rtol)
+    @test isapprox(mean(w_inact.I_h), 0.18217582636296747, rtol = rtol)
+    @test isapprox(mean(firms.I_h), 0.7442975169996757, rtol = rtol)
+    @test isapprox(bank.I_h, 233.5381841004737, rtol = rtol)
+
+    @test isapprox(gov.C_j, 14686.094833493271, rtol = rtol)
+    @test isapprox(rotw.C_l, 44241.742486622454, rtol = rtol)
+
+    @test isapprox(mean(firms.I_i), 20.671016463479898, rtol = rtol)
+    @test isapprox(mean(firms.DM_i), 110.18635469222951, rtol = rtol)
+    @test isapprox(mean(firms.P_bar_i), 1.0010000000000023, rtol = rtol)
+    @test isapprox(mean(firms.P_CF_i), 1.0010000000000023, rtol = rtol)
+    @test isapprox(mean(firms.Q_d_i), 216.70740037345882, rtol = rtol)
+    @test isapprox(mean(rotw.Q_d_m), 719.2385742449192, rtol = rtol)
+end