123 lines
5.0 KiB
Matlab
123 lines
5.0 KiB
Matlab
function [endogenousvariables, exogenousvariables] = model_inversion(constraints, ...
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exogenousvariables, ...
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initialconditions, M_, options_, oo_)
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% function [endogenousvariables, exogenousvariables] = model_inversion(constraints, ...
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% exogenousvariables, ...
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% initialconditions, M_, options_, oo_)
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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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% - M_ [struct] Dynare global structure containing informations related to the model.
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% - options_ [struct] Dynare global structure containing all the options.
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% - oo_ [struct] Dynare global structure containing all the options.
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%
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% OUTPUTS
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% - endogenousvariables [dseries]
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% - exogenousvariables [dseries]
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%
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% REMARKS
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% Copyright © 2018-2023 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 <https://www.gnu.org/licenses/>.
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if ~isequal(nargin, 6)
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error('model_inversion: This routine require six input arguments!')
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end
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if ~isdseries(constraints)
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error('model_inversion: First input argument must be a dseries object!')
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end
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if ~isdseries(exogenousvariables)
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error('model_inversion: Second input argument must be a dseries object!')
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end
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if ~isempty(initialconditions) && ~isdseries(initialconditions)
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error('model_inversion: Third input argument must be a dseries object!')
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end
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if ~isstruct(M_)
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error('model_inversion: Last input argument must be structures (M_)!')
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end
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% Set range where the endogenous variables are constrained.
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crange = constraints.dates;
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% Check that the number of instruments match the number of constrained endogenous variables.
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instruments = exogenousvariables(crange);
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freeinnovations = instruments.name(find(all(isnan(instruments))));
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if ~isequal(length(freeinnovations), constraints.vobs)
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error('The number of instruments must be equal to the number of constrained variables!')
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end
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% Check if some of the exogenous variables are given.
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observed_exogenous_variables_flag = false;
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if exogenousvariables.vobs>constraints.vobs
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observed_exogenous_variables_flag = true;
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end
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if M_.maximum_lag
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% Add auxiliary variables in initialconditions object.
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initialconditions = checkdatabase(initialconditions, M_, true, false);
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end
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% Get the list of endogenous and exogenous variables.
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endo_names = M_.endo_names;
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exo_names = M_.exo_names;
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exogenousvariables = exogenousvariables{exo_names{:}};
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% Use specidalized routine if the model is backward looking.
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if ~M_.maximum_lead
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if M_.maximum_lag
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[endogenousvariables, exogenousvariables] = ...
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backward_model_inversion(constraints, exogenousvariables, initialconditions, ...
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endo_names, exo_names, freeinnovations, ...
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M_, options_, oo_);
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else
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[endogenousvariables, exogenousvariables] = ...
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static_model_inversion(constraints, exogenousvariables, ...
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endo_names, exo_names, freeinnovations, ...
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M_, options_, oo_);
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end
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return
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end
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% Initialize fplan
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fplan = init_plan(crange);
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% Set the exogenous observed variables.
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if observed_exogenous_variables_flag
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list_of_observed_exogenous_variables = setdiff(exo_names, freeinnovations);
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observed_exogenous_variables = exogenousvariables{list_of_observed_exogenous_variables{:}};
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for i=1:length(list_of_observed_exogenous_variables)
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fplan = basic_plan(fplan, list_of_observed_exogenous_variables{i}, ...
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'surprise', crange, observed_exogenous_variables{list_of_observed_exogenous_variables{i}}.data(2:length(crange)+1));
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end
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end
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% Set constrained path for the endogenous variables.
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for i = 1:constraints.vobs
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fplan = flip_plan(fplan, constraints.name{i}, freeinnovations{i}, 'surprise', crange, transpose(constraints.data(:,i)));
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end
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% Identify the innovations (model inversion)
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f = det_cond_forecast(fplan, initialconditions, crange);
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endogenousvariables = f{endo_names{:}};
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exogenousvariables = f{exo_names{:}}; |