Essential use of BeforeIT

We start by importing the BeforeIT library and other useful libraries.

import BeforeIT as Bit

using Plots

We then initialise the model loading some precomputed set of parameters and by specifying a number of epochs. In another tutorial we will illustrate how to compute parameters and initial conditions.

parameters = Bit.AUSTRIA2010Q1.parameters
initial_conditions = Bit.AUSTRIA2010Q1.initial_conditions

Dict{String, Any} with 21 entries:
  "sb_inact" => 2.23847
  "C_G"      => [11482.3; 11556.2; … ; 14578.2; 14714.8;;]
  "E_CB"     => 1.0618e5
  "D_RoW"    => 0.0
  "sb_other" => 0.590286
  "L_I"      => 236919.0
  "omega"    => 0.85
  "r_bar"    => 0.00164593
  "N_s"      => [123.0; 18.0; … ; 5.0; 60.0;;]
  "C_E"      => [17384.5; 17752.9; … ; 38786.7; 38617.8;;]
  "D_H"      => 219841.0
  "K_H"      => 4.05377e5
  "L_G"      => 2.32611e5
  "pi"       => [-0.00749736; -0.00789543; … ; 0.00530319; 0.00100796;;]
  "w_UB"     => 4.06546
  "Y"        => [1.04531e5; 1.05062e5; … ; 1.35709e5; 1.34636e5;;]
  "Y_I"      => [19804.6; 19944.1; … ; 36883.2; 36575.9;;]
  "Y_EA"     => 2.35485e6
  "D_I"      => 54049.0
  ⋮          => ⋮

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);

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.step!(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 = Bit.run!(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)

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))

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

model = Bit.init_model(parameters, initial_conditions, T)
data_vector = Bit.ensemblerun(model, 4)

BeforeIT.DataVector{BeforeIT.Data}(BeforeIT.Data[BeforeIT.Data{Float64}([72421.99999999999, 73620.72007800141, 74438.36640006029, 74593.22090959377, 74960.62998975781, 74970.75294322471, 74065.2632462753, 74550.02975729897, 74773.00913556597, 74368.80640390523, 75213.96937452328, 75416.23494538966, 74869.59871239786, 75789.748783867, 75878.0991640187, 76231.13509783823, 77016.92436943088], [72421.99999999999, 72964.29477125614, 73534.30591553754, 73550.7402937265, 73929.59295115442, 73645.91747090333, 72681.36501131477, 73072.41734742785, 73649.76920644598, 73167.36234316025, 74167.31291393525, 74051.8697529632, 74134.23607251648, 74626.6565903346, 74514.18330962623, 75096.26946271438, 76099.4496588541], [64900.92049553802, 66007.04330642606, 66749.27408370194, 66891.2597723325, 67216.52968979077, 67217.55107964121, 66418.16917255144, 66860.2151849482, 67068.74830996832, 66731.9888685014, 67492.26886542242, 67673.1261208515, 67201.5531008089, 68021.86170372354, 68105.86387531605, 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258.24326637797435 … 136.99722836422484 504.9046295086172; 520.8817331718842 259.63810080535706 … 137.73718357490102 507.6317416988444; … ; 526.408430178084 267.04263352522327 … 144.8595569681936 537.8011385559485; 515.0460944639158 262.7719440670136 … 144.07026115929816 532.0700541873052], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086172; 520.3959765521272 259.3959711275903 … 137.608734553852 507.1583415712807; … ; 497.67528928714376 252.46654930413263 … 136.95263560988988 508.44614536138977; 486.22723728420743 248.06881902966327 … 136.00896271470992 502.2986394226053])])

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))