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, Vector{Float64}, Matrix{Float64}}([72421.99999999997, 72541.14913269176, 71966.16261629357, 71316.93526457544, 72082.24053558064, 71678.13431859808, 71586.97511788069, 72875.12592993767, 72992.45069032314, 73321.83167801354, 74679.11361119062, 75540.69346445995, 75271.17152648425, 76571.07240452767, 77187.16508968611, 77566.42823073087, 77658.37659572501], [72421.99999999997, 72351.70820718989, 71940.69978544631, 71512.56409630165, 72041.46850801031, 71558.23830417093, 71446.03624571417, 72213.99702238229, 72237.1748955783, 72846.68642145979, 73849.42556286897, 74385.76513544934, 74104.66874872793, 75203.6533978156, 75384.20968819193, 75621.86646291765, 75640.78900087062], [64900.92049553801, 65012.8340812113, 64502.61497087743, 63929.50641798554, 64632.50065783851, 64270.8747433738, 64239.5379528272, 65412.99620863827, 65526.897118414054, 65842.43729287446, 67055.22357343785, 67858.53346985263, 67609.62409851069, 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1039.5298399952972, 1050.4238535052748], [0.0019383188997990075, 0.006347910192240347, 0.003511342920600846, 0.006601140396663485, 0.0031021808134645834, 0.008391365533865214, 0.006142424492080689, 0.009903881389609515, 0.0071984062198646726, 0.006318502717966412, 0.004342054163132714, 0.005575381196158968, 0.0035375040561289595, 0.0029311775037901278, 0.007802843178355223, 0.0033299658623671125, 0.004613514155459075], [2.3548476e6, 2.372528402238888e6, 2.361951510727643e6, 2.3487588206945495e6, 2.3662681081835525e6, 2.3705501514219656e6, 2.3766187279380024e6, 2.3755548009415814e6, 2.352248029052681e6, 2.33140071860467e6, 2.3243506255484247e6, 2.3497649466868024e6, 2.3547230326657034e6, 2.387532708924215e6, 2.4107170376087874e6, 2.410888477813864e6, 2.41023472702334e6], [0.0016459319014481277, 0.0024082621028785074, 0.0018720541331077463, 0.0013355535037540011, 0.0020411107645820496, 0.0022605602337772753, 0.002485960510720613, 0.0024851594390709873, 0.0015013468841117935, 0.0006634584116033412, 0.00041654433909371787, 0.0015891122741944037, 0.0017601869362100092, 0.0030676435187181514, 0.0039747281215282435, 0.0037638776307966546, 0.003564425164473683], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086171; 520.6614405555903 259.5282940049384 … 137.67893141784032 507.417052802361; … ; 536.2589328677536 270.2171880005276 … 142.71860878401222 526.753816032972; 515.2932823419532 264.3594408324014 … 142.5078200364934 524.6369148451672], [518.0834390395164 258.24326637797435 … 136.99722836422484 504.9046295086171; 523.1555586286895 260.7715091888159 … 138.3384531038861 509.8477264482579; … ; 531.9063152546587 268.0239339216398 … 141.5602140380492 522.4783479765813; 509.7429942639235 261.5119923927516 … 140.97285057021256 518.9859853385276])])

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))