121 lines
5.4 KiB
Matlab
121 lines
5.4 KiB
Matlab
function [endogenousvariables, exogenousvariables] = backward_model_inversion(constraints, exogenousvariables, initialconditions, endo_names, exo_names, freeinnovations, DynareModel, DynareOptions, DynareOutput)
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% INPUTS
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% - constraints [dseries] with N constrained endogenous variables from t1 to t2.
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% - exogenousvariables [dseries] with Q exogenous variables.
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% - initialconditions [dseries] with M endogenous variables starting before t1 (M initialcond must contain at least the state variables).
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% - endo_names [cell] list of endogenous variable names.
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% - exo_names [cell] list of exogenous variable names.
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% - freeinstruments [cell] list of exogenous variable names used to control the constrained endogenous variables.
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%
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% OUTPUTS
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% - endogenous [dseries]
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% - exogenous [dseries]
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%
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% REMARKS
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% Copyright (C) 2017 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% Get indices for the calibrated and free innovations.
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freeinnovations_id = zeros(length(freeinnovations), 1);
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if length(freeinnovations)<DynareModel.exo_nbr
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for i=1:length(freeinnovations)
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freeinnovations_id(i) = strmatch(freeinnovations{i}, exo_names, 'exact');
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end
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calibratedinnovations_id = setdiff(transpose(1:length(exo_names)), freeinnovations_id);
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else
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freeinnovations_id = transpose(1:length(exo_names));
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calibratedinnovations_id = [];
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end
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nxfree = length(freeinnovations_id);
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nxcalb = length(calibratedinnovations_id);
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% Get indices for the the controlled and free endogenous variables.
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controlledendogenousvariables_id = zeros(length(freeinnovations), 1);
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if length(freeinnovations)<DynareModel.endo_nbr
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for i=1:length(freeinnovations)
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controlledendogenousvariables_id(i) = strmatch(constraints.name{i}, endo_names, 'exact');
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end
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freeendogenousvariables_id = setdiff(transpose(1:length(endo_names)), controlledendogenousvariables_id);
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else
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controlledendogenousvariables_id = transpose(1:length(endo_names));
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freeendogenousvariables_id = [];
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end
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nyfree = length(freeendogenousvariables_id);
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nyctrl = length(controlledendogenousvariables_id);
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% Get indices of variables appearing at time t-1.
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iy1 = find(DynareModel.lead_lag_incidence(1,:)>0);
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% Get indices of variables appearing at time t.
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iy0 = find(DynareModel.lead_lag_incidence(2,:)>0);
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% Set indices for trust_region algorithm.
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idx = 1:DynareModel.endo_nbr;
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jdx = 1:(nyfree+nxfree);
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% Build structure to be passed to the objective function.
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ModelInversion.nyfree = nyfree;
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ModelInversion.nyctrl = nyctrl;
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ModelInversion.nxfree = nxfree;
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ModelInversion.nxcalb = nxcalb;
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ModelInversion.y_constrained_id = vec(DynareModel.lead_lag_incidence(2,controlledendogenousvariables_id));
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ModelInversion.y_free_id = vec(DynareModel.lead_lag_incidence(2,freeendogenousvariables_id));
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ModelInversion.x_free_id = freeinnovations_id;
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ModelInversion.J_id = [ModelInversion.y_free_id ; sum(DynareModel.lead_lag_incidence(:)>0)+ModelInversion.x_free_id];
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% Get the name of the dynamic model routines.
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model_dynamic = str2func([DynareModel.fname,'_dynamic']);
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model_dtransf = str2func('dynamic_backward_model_for_inversion');
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% Initialization of vector y (free endogenous variables and free innovations).
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y = NaN(nyfree+nxfree);
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% Initialization of the returned simulations (endogenous variables).
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Y = NaN(DynareModel.endo_nbr, nobs(constraints)+1);
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initialconditions
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constraints.dates(1)
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Y(:,1) = initialconditions(constraints.dates(1)-1).data(1:DynareModel.endo_nbr);
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for i=1:nyctrl
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Y(controlledendogenousvariables_id(i),2:end) = transpose(constraints.data(:,i));
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end
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% Initialization of the returned simulations (exogenous variables).
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X = exogenousvariables{exo_names{:}}(constraints.dates(1)-1:constraints.dates(end)).data;
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% Inversion of the model, solvers for the free endogenous and exogenous variables (call a Newton-like algorithm in each period).
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for it = 2:nobs(constraints)+1
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% Set the lagged values of the endogenous variables.
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ylag = Y(iy1,it-1);
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% Set the current values of the constrained endogenous variables.
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ycur = Y(controlledendogenousvariables_id,it);
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% Vector z gather the free endogenous variables (initialized with lagged
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% values) and the free exogenous variables (initialized with 0).
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z = [Y(freeendogenousvariables_id,it-1); zeros(nxfree, 1)];
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% Solves for z.
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z = dynare_solve(model_dtransf, z, DynareOptions, model_dynamic, ylag, ycur, X, DynareModel.params, DynareOutput.steady_state, it, ModelInversion);
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% Update the matrix of exogenous variables.
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X(it,freeinnovations_id) = z(nyfree+1:end);
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% Update the matrix of endogenous variables.
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Y(freeendogenousvariables_id,it) = z(1:nyfree);
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end
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endogenousvariables = dseries(Y', constraints.dates(1)-1, endo_names);
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exogenousvariables = dseries(X(2:end,:), constraints.dates(1), exo_names); |