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Merge branch 'model_inversion' of git.dynare.org:JohannesPfeifer/dynare 

Ref. !2080
bgp-dev
Sébastien Villemot 2022-09-19 16:22:42 +02:00
parent 5a3f50bf2e f91e4e239c
commit ef5f25c17a
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5 changed files with 27 additions and 14 deletions
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4
matlab/backward/simul_backward_model.m
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@ -1,5 +1,5 @@
function simulation = simul_backward_model(initialconditions, samplesize, innovations)
% function simulation = simul_backward_model(initialconditions, samplesize, innovations)
% Simulates a stochastic backward looking model (with arbitrary precision).
%
% INPUTS
@ -16,7 +16,7 @@ function simulation = simul_backward_model(initialconditions, samplesize, innova
% [2] The last input argument is not mandatory. If absent we use random draws and rescale them with the informations provided
% through the shocks block.
% [3] If the first input argument is empty, the endogenous variables are initialized with 0, or if available with the informations
% provided thrtough the histval block.
% provided through the histval block.
% Copyright © 2012-2022 Dynare Team
%

4
matlab/backward/simul_backward_nonlinear_model.m
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@ -1,5 +1,5 @@
function simulations = simul_backward_nonlinear_model(initialconditions, samplesize, DynareOptions, DynareModel, DynareOutput, innovations)
% function simulations = simul_backward_nonlinear_model(initialconditions, samplesize, DynareOptions, DynareModel, DynareOutput, innovations)
% Simulates a stochastic non linear backward looking model with arbitrary precision (a deterministic solver is used).
%
% INPUTS
@ -11,7 +11,7 @@ function simulations = simul_backward_nonlinear_model(initialconditions, samples
% - innovations [double] T*q matrix, innovations to be used for the simulation.
%
% OUTPUTS
% - DynareOutput [struct] Dynare's oo_ global structure.
% - simulation [dseries] Simulated endogenous and exogenous variables.
%
% REMARKS
% [1] The innovations used for the simulation are saved in DynareOutput.exo_simul, and the resulting paths for the endogenous

11
matlab/model_inversion.m
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@ -1,21 +1,24 @@
function [endogenousvariables, exogenousvariables] = model_inversion(constraints, ...
exogenousvariables, ...
initialconditions, DynareModel, DynareOptions, DynareOutput)
% function [endogenousvariables, exogenousvariables] = model_inversion(constraints, ...
% exogenousvariables, ...
% initialconditions, DynareModel, DynareOptions, DynareOutput)
% INPUTS
% - constraints [dseries] with N constrained endogenous variables from t1 to t2.
% - exogenousvariables [dseries] with Q exogenous variables.
% - initialconditions [dseries] with M endogenous variables starting before t1 (M initialcond must contain at least the state variables).
% - DynareModel [struct] M_, Dynare global structure containing informations related to the model.
% - DynareOptions [struct] options_, Dynare global structure containing all the options.
% - DynareOutput [struct] oo_, Dynare global structure containing all the options.
%
% OUTPUTS
% - endogenous [dseries]
% - exogenous [dseries]
% - endogenousvariables [dseries]
% - exogenousvariables [dseries]
%
% REMARKS
% Copyright © 2018-2019 Dynare Team
% Copyright © 2018-2021 Dynare Team
%
% This file is part of Dynare.
%

10
tests/model-inversion/bk-1/solow_ces.mod
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@ -113,6 +113,12 @@ TruePopulation = TrueData.Population;
TrueEfficiencyTimesPopulation = TrueEfficiency*TruePopulation;
EfficiencyTimesPopulation = endogenousvariables.Efficiency*endogenousvariables.Population;
if max(abs(TrueEfficiencyTimesPopulation(2Y:100Y).data-EfficiencyTimesPopulation(2Y:100Y).data))>1e-5
error('Model inversion is not consitent with true innovations.')
if isoctave
tolerance=2e-5;
else
tolerance=1e-5;
end
if max(abs(TrueEfficiencyTimesPopulation(2Y:100Y).data-EfficiencyTimesPopulation(2Y:100Y).data))>tolerance
error('Model inversion is not consistent with true innovations.')
end

12
tests/model-inversion/bk-3b-nlsolve/varmodel.mod
View File

@ -70,8 +70,12 @@ steady;
check;
TrueData = simul_backward_model([], 200);
if isoctave
options_.bnlms.set_dynare_seed_to_default=false;
set_dynare_seed(1);
end
TrueData = simul_backward_model([], 200);
// Set the periods where some of the endogenous variables will be constrained.
subsample = 3Y:100Y;
@ -93,11 +97,11 @@ if max(abs(constrainedpaths(subsample).y1.data-endogenousvariables(subsample).y1
end
if max(abs(exogenousvariables(subsample).e2.data-SimulatedData(subsample).e2.data))>1e-12
error('Constraint on e1 path is not satisfied!')
error('Constraint on e2 path is not satisfied!')
end
if max(abs(exogenousvariables(subsample).e3.data-SimulatedData(subsample).e3.data))>1e-12
error('Constraint on e2 path is not satisfied!')
error('Constraint on e3 path is not satisfied!')
end
// Check consistency of the results.
@ -110,5 +114,5 @@ if max(abs(SimulatedData(subsample).y3.data-endogenousvariables(subsample).y3.da
end
if max(abs(exogenousvariables(subsample).e1.data-SimulatedData(subsample).e1.data))>1e-12
error('Model inversion is not consistent with true innovations (e3)')
error('Model inversion is not consistent with true innovations (e1)')
end