208 lines
8.7 KiB
Matlab
208 lines
8.7 KiB
Matlab
function [initialconditions, samplesize, innovations, DynareOptions, DynareModel, DynareOutput, endonames, exonames, nx, ny1, iy1, jdx, model_dynamic, y] = simul_backward_model_init(varargin)
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% Initialization of the routines simulating backward models.
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% Copyright (C) 2017-2018 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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initialconditions = varargin{1};
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if ~isdseries(initialconditions)
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error('First input argument must be a dseries object')
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end
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samplesize = varargin{2};
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DynareOptions = varargin{3};
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DynareModel = varargin{4};
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DynareOutput = varargin{5};
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% Test if the model is backward.
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if DynareModel.maximum_lead
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error('simul_backward_nonlinear_model:: The specified model is not backward looking!')
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end
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% Test if the first argument is a dseries object.
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if ~isdseries(initialconditions)
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error('First input argument must be a dseries object!')
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end
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% Test if the first argument contains all the lagged endogenous variables
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endonames = DynareModel.endo_names;
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missingendogenousvariables = setdiff(endonames, initialconditions.name);
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endolags = get_lags_on_endogenous_variables(DynareModel);
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endolags_ = endolags(find(endolags));
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endowithlagnames = endonames(find(endolags));
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if ~isempty(missingendogenousvariables)
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missingendogenousvariables = setdiff(endowithlagnames, initialconditions.name);
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missingendogenouslaggedvariables = intersect(endowithlagnames, missingendogenousvariables);
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if ~isempty(missingendogenouslaggedvariables)
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disp('You have to initialize the following endogenous variables:')
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msg = sprintf('%s\n', missingendogenouslaggedvariables{1:end-1});
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msg = sprintf('%s%s', msg, missingendogenouslaggedvariables{end});
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disp(msg)
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skipline()
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error('Please fix the dseries object used for setting the initial conditions!')
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end
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end
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% Test if we have enough periods in the database.
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maxlag = abs(min(endolags));
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if maxlag>initialconditions.nobs
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error('The dseries object provided as first input argument should at least have %s periods!', num2str(maxlag))
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end
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missinginitialcondition = false;
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for i = 1:length(endowithlagnames)
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lags = abs(endolags_(i));
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variable = initialconditions{endowithlagnames{i}};
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nanvalues = isnan(variable.data);
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if any(nanvalues(end-(lags-1):end))
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missinginitialcondition = true;
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for j=variable.nobs:-1:variable.nobs-(lags-1)
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if isnan(variable.data(j))
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disp(sprintf('Variable %s should not have a NaN value in period %s.', endowithlagnames{i}, date2string(variable.dates(j))))
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end
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end
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end
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end
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if missinginitialcondition
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skipline()
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error('Please fix the dseries object used for setting the initial conditions!')
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end
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% If the model has lags on the exogenous variables, test if we have corresponding initial conditions.
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exonames = DynareModel.exo_names;
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missingexogenousvariables = setdiff(exonames, initialconditions.name);
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exolags = get_lags_on_exogenous_variables(DynareModel);
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exolags_ = exolags(find(exolags));
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exowithlagnames = exonames(find(exolags));
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if ~isempty(missingexogenousvariables)
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missingexogenousvariables = setdiff(exowithlagnames, initialconditions.name);
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missingexogenouslaggedvariables = intersect(exowithlagnames, missingexogenousvariables);
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if ~isempty(missingexogenouslaggedvariables)
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disp('You have to initialize the following exogenous variables:')
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msg = sprintf('%s\n', missingexogenouslaggedvariables{1:end-1});
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msg = sprintf('%s%s', msg, missingexogenouslaggedvariables{end});
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disp(msg)
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skipline()
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error('Please fix the dseries object used for setting the initial conditions!')
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end
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end
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% Test if we have enough periods in the database.
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maxlag = abs(min(exolags));
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if maxlag>initialconditions.nobs
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error('The dseries object provided as first input argument should at least have %s periods!', num2str(maxlag))
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end
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missinginitialcondition = false;
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for i = 1:length(exowithlagnames)
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lags = abs(exolags_(i));
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variable = initialconditions{exowithlagnames{i}};
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nanvalues = isnan(variable.data);
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if any(nanvalues(end-(lags-1):end))
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missinginitialcondition = true;
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for j=variable.nobs:-1:variable.nobs-(lags-1)
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if isnan(variable.data(j))
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disp(sprintf('Variable %s should not have a NaN value in period %s.', exowithlagnames{i}, date2string(variable.dates(j))))
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end
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end
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end
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end
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if missinginitialcondition
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skipline()
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error('Please fix the dseries object used for setting the initial conditions!')
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end
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% Add auxiliary variables to the database.
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k = 0;
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for i = DynareModel.orig_endo_nbr+1:DynareModel.endo_nbr
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k = k+1;
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if DynareModel.aux_vars(k).type==1
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if ismember(DynareModel.endo_names{DynareModel.aux_vars(k).orig_index}, initialconditions.name)
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initialconditions{DynareModel.endo_names{DynareModel.aux_vars(k).endo_index}} = ...
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initialconditions{DynareModel.endo_names{DynareModel.aux_vars(k).orig_index}}.lag(abs(DynareModel.aux_vars(k).orig_lead_lag));
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else
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error('This is a bug. Please contact Dynare Team!');
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end
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elseif DynareModel.aux_vars(k).type==3
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if ismember(DynareModel.exo_names{DynareModel.aux_vars(k).orig_index}, initialconditions.name)
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initialconditions{DynareModel.endo_names{DynareModel.aux_vars(k).endo_index}} = ...
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initialconditions{DynareModel.exo_names{DynareModel.aux_vars(k).orig_index}}.lag(abs(DynareModel.aux_vars(k).orig_lead_lag));
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else
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error('This is a bug. Please contact Dynare Team!');
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end
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else
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error('Cannot simulate the model with this type of auxiliary variables!')
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end
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end
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if nargin<6 || isempty(varargin{6})
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% Set the covariance matrix of the structural innovations.
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variances = diag(DynareModel.Sigma_e);
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number_of_shocks = length(DynareModel.Sigma_e);
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positive_var_indx = find(variances>0);
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effective_number_of_shocks = length(positive_var_indx);
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covariance_matrix = DynareModel.Sigma_e(positive_var_indx,positive_var_indx);
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covariance_matrix_upper_cholesky = chol(covariance_matrix);
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% Set seed to its default state.
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if DynareOptions.bnlms.set_dynare_seed_to_default
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set_dynare_seed('default');
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end
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% Simulate structural innovations.
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switch DynareOptions.bnlms.innovation_distribution
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case 'gaussian'
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DynareOutput.bnlms.shocks = randn(samplesize,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
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otherwise
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error(['simul_backward_nonlinear_model:: ' DynareOption.bnlms.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
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end
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% Put the simulated innovations in DynareOutput.exo_simul.
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DynareOutput.exo_simul = zeros(samplesize,number_of_shocks);
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DynareOutput.exo_simul(:,positive_var_indx) = DynareOutput.bnlms.shocks;
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innovations = DynareOutput.exo_simul;
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else
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innovations = varargin{6};
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DynareOutput.exo_simul = innovations; % innovations
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end
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% Initialization of the returned simulations.
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DynareOutput.endo_simul = NaN(DynareModel.endo_nbr, samplesize+initialconditions.nobs);
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for i=1:length(endonames)
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if ismember(endonames{i}, initialconditions.name)
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DynareOutput.endo_simul(i,1:initialconditions.nobs) = transpose(initialconditions{endonames{i}}.data);
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end
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end
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% Initialization of the array for the exogenous variables.
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DynareOutput.exo_simul = [NaN(initialconditions.nobs, DynareModel.exo_nbr); DynareOutput.exo_simul ];
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for i=1:length(exonames)
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if ismember(exonames{i}, initialconditions.name)
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DynareOutput.exo_simul(1:initialconditions.nobs, i) = initialconditions{exonames{i}}.data;
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end
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end
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if nargout>8
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nx = size(DynareOutput.exo_simul,2);
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ny0 = nnz(DynareModel.lead_lag_incidence(2,:));
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ny1 = nnz(DynareModel.lead_lag_incidence(1,:));
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iy1 = find(DynareModel.lead_lag_incidence(1,:)>0);
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idx = 1:DynareModel.endo_nbr;
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jdx = idx+ny1;
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% Get the name of the dynamic model routine.
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model_dynamic = str2func([DynareModel.fname,'_dynamic']);
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% initialization of vector y.
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y = NaN(length(idx)+ny1,1);
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end |