199 lines
8.7 KiB
Matlab
199 lines
8.7 KiB
Matlab
function [initialconditions, samplesize, innovations, DynareOptions, DynareModel, DynareOutput, endonames, exonames, dynamic_resid, dynamic_g1, y] = ...
|
|
simul_backward_model_init(initialconditions, samplesize, DynareOptions, DynareModel, DynareOutput, innovations)
|
|
|
|
% Initialization of the routines simulating backward models.
|
|
|
|
% Copyright © 2017-2023 Dynare Team
|
|
%
|
|
% This file is part of Dynare.
|
|
%
|
|
% Dynare is free software: you can redistribute it and/or modify
|
|
% it under the terms of the GNU General Public License as published by
|
|
% the Free Software Foundation, either version 3 of the License, or
|
|
% (at your option) any later version.
|
|
%
|
|
% Dynare is distributed in the hope that it will be useful,
|
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
% GNU General Public License for more details.
|
|
%
|
|
% You should have received a copy of the GNU General Public License
|
|
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
|
|
|
% Test if the model is backward.
|
|
if DynareModel.maximum_lead
|
|
error('simul_backward_nonlinear_model:: The specified model is not backward looking!')
|
|
end
|
|
|
|
% Test if the first argument is a dseries object.
|
|
if ~(isdseries(initialconditions) || isempty(initialconditions))
|
|
error('First input argument must be a dseries object or an empty array!')
|
|
end
|
|
|
|
% If initialconditions is empty instantiates a dseries object with the informations available in DynareModel.endo_histval.
|
|
if isempty(initialconditions)
|
|
yinitdata = zeros(DynareModel.orig_endo_nbr, DynareModel.orig_maximum_lag);
|
|
yinitdata(:,1) = DynareModel.endo_histval(1:DynareModel.orig_endo_nbr);
|
|
xinitdata = zeros(DynareModel.exo_nbr, DynareModel.orig_maximum_lag);
|
|
if DynareModel.orig_maximum_endo_lag>1
|
|
for i = 1:length(DynareModel.aux_vars)
|
|
if DynareModel.aux_vars(i).type==1
|
|
yinitdata(DynareModel.aux_vars(i).orig_index, abs(DynareModel.aux_vars(i).orig_lead_lag)+1) = ...
|
|
DynareModel.endo_histval(DynareModel.orig_endo_nbr+i);
|
|
end
|
|
end
|
|
yinitdata = flip(yinitdata, 2);
|
|
end
|
|
if DynareModel.orig_maximum_exo_lag>0
|
|
for i = 1:length(DynareModel.aux_vars)
|
|
if DynareModel.aux_vars(i).type==3
|
|
xinitdata(DynareModel.aux_vars(i).orig_index, abs(DynareModel.aux_vars(i).orig_lead_lag)+1) = ...
|
|
DynareModel.endo_histval(DynareModel.orig_endo_nbr+i);
|
|
end
|
|
end
|
|
xinitdata = flip(xinitdata, 2);
|
|
end
|
|
initialconditions = dseries([transpose(yinitdata) transpose(xinitdata)], '1Y', ...
|
|
vertcat(DynareModel.endo_names(1:DynareModel.orig_endo_nbr), DynareModel.exo_names));
|
|
end
|
|
|
|
[initialconditions, info] = checkdatabase(initialconditions, DynareModel, false, true);
|
|
|
|
% Test if the first argument contains all the lagged endogenous variables
|
|
endonames = DynareModel.endo_names;
|
|
missingendogenousvariables = setdiff(endonames, initialconditions.name);
|
|
endolags = get_lags_on_endogenous_variables(DynareModel);
|
|
endolags_ = endolags(find(endolags));
|
|
endowithlagnames = endonames(find(endolags));
|
|
if ~isempty(missingendogenousvariables)
|
|
missingendogenousvariables = setdiff(endowithlagnames, initialconditions.name);
|
|
missingendogenouslaggedvariables = intersect(endowithlagnames, missingendogenousvariables);
|
|
if ~isempty(missingendogenouslaggedvariables)
|
|
disp('You have to initialize the following endogenous variables:')
|
|
msg = sprintf('%s\n', missingendogenouslaggedvariables{1:end-1});
|
|
msg = sprintf('%s%s', msg, missingendogenouslaggedvariables{end});
|
|
disp(msg)
|
|
skipline()
|
|
error('Please fix the dseries object used for setting the initial conditions!')
|
|
end
|
|
end
|
|
|
|
% Test if we have enough periods in the database.
|
|
maxlag = abs(min(endolags));
|
|
if maxlag>initialconditions.nobs
|
|
error('The dseries object provided as first input argument should at least have %s periods!', num2str(maxlag))
|
|
end
|
|
missinginitialcondition = false;
|
|
for i = 1:length(endowithlagnames)
|
|
lags = abs(endolags_(i));
|
|
variable = initialconditions{endowithlagnames{i}};
|
|
nanvalues = isnan(variable.data);
|
|
if any(nanvalues(end-(lags-1):end))
|
|
missinginitialcondition = true;
|
|
for j=variable.nobs:-1:variable.nobs-(lags-1)
|
|
if isnan(variable.data(j))
|
|
dprintf('Variable %s should not have a NaN value in period %s.', endowithlagnames{i}, date2string(variable.dates(j)))
|
|
end
|
|
end
|
|
end
|
|
end
|
|
if missinginitialcondition
|
|
skipline()
|
|
error('Please fix the dseries object used for setting the initial conditions!')
|
|
end
|
|
|
|
% If the model has lags on the exogenous variables, test if we have corresponding initial conditions.
|
|
exonames = DynareModel.exo_names;
|
|
missingexogenousvariables = setdiff(exonames, initialconditions.name);
|
|
exolags = get_lags_on_exogenous_variables(DynareModel);
|
|
exolags_ = exolags(find(exolags));
|
|
exowithlagnames = exonames(find(exolags));
|
|
if ~isempty(missingexogenousvariables)
|
|
missingexogenousvariables = setdiff(exowithlagnames, initialconditions.name);
|
|
missingexogenouslaggedvariables = intersect(exowithlagnames, missingexogenousvariables);
|
|
if ~isempty(missingexogenouslaggedvariables)
|
|
disp('You have to initialize the following exogenous variables:')
|
|
msg = sprintf('%s\n', missingexogenouslaggedvariables{1:end-1});
|
|
msg = sprintf('%s%s', msg, missingexogenouslaggedvariables{end});
|
|
disp(msg)
|
|
skipline()
|
|
error('Please fix the dseries object used for setting the initial conditions!')
|
|
end
|
|
end
|
|
|
|
% Test if we have enough periods in the database.
|
|
maxlag = abs(min(exolags));
|
|
if maxlag>initialconditions.nobs
|
|
error('The dseries object provided as first input argument should at least have %s periods!', num2str(maxlag))
|
|
end
|
|
missinginitialcondition = false;
|
|
for i = 1:length(exowithlagnames)
|
|
lags = abs(exolags_(i));
|
|
variable = initialconditions{exowithlagnames{i}};
|
|
nanvalues = isnan(variable.data);
|
|
if any(nanvalues(end-(lags-1):end))
|
|
missinginitialcondition = true;
|
|
for j=variable.nobs:-1:variable.nobs-(lags-1)
|
|
if isnan(variable.data(j))
|
|
dprintf('Variable %s should not have a NaN value in period %s.', exowithlagnames{i}, date2string(variable.dates(j)))
|
|
end
|
|
end
|
|
end
|
|
end
|
|
if missinginitialcondition
|
|
skipline()
|
|
error('Please fix the dseries object used for setting the initial conditions!')
|
|
end
|
|
|
|
if nargin<6 || isempty(innovations)
|
|
% Set the covariance matrix of the structural innovations.
|
|
variances = diag(DynareModel.Sigma_e);
|
|
number_of_shocks = length(DynareModel.Sigma_e);
|
|
positive_var_indx = find(variances>0);
|
|
effective_number_of_shocks = length(positive_var_indx);
|
|
covariance_matrix = DynareModel.Sigma_e(positive_var_indx,positive_var_indx);
|
|
covariance_matrix_upper_cholesky = chol(covariance_matrix);
|
|
% Set seed to its default state.
|
|
if DynareOptions.bnlms.set_dynare_seed_to_default
|
|
DynareOptions=set_dynare_seed_local_options(DynareOptions,'default');
|
|
end
|
|
% Simulate structural innovations.
|
|
switch DynareOptions.bnlms.innovation_distribution
|
|
case 'gaussian'
|
|
DynareOutput.bnlms.shocks = randn(samplesize,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
|
|
otherwise
|
|
error(['simul_backward_nonlinear_model:: ' DynareOptions.bnlms.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
|
|
end
|
|
% Put the simulated innovations in DynareOutput.exo_simul.
|
|
DynareOutput.exo_simul = zeros(samplesize,number_of_shocks);
|
|
DynareOutput.exo_simul(:,positive_var_indx) = DynareOutput.bnlms.shocks;
|
|
innovations = DynareOutput.exo_simul;
|
|
else
|
|
DynareOutput.exo_simul = innovations; % innovations
|
|
end
|
|
|
|
% Initialization of the returned simulations.
|
|
DynareOutput.endo_simul = NaN(DynareModel.endo_nbr, samplesize+initialconditions.nobs);
|
|
for i=1:length(endonames)
|
|
if ismember(endonames{i}, initialconditions.name)
|
|
DynareOutput.endo_simul(i,1:initialconditions.nobs) = transpose(initialconditions{endonames{i}}.data);
|
|
end
|
|
end
|
|
|
|
% Initialization of the array for the exogenous variables.
|
|
DynareOutput.exo_simul = [NaN(initialconditions.nobs, DynareModel.exo_nbr); DynareOutput.exo_simul ];
|
|
for i=1:length(exonames)
|
|
if ismember(exonames{i}, initialconditions.name)
|
|
DynareOutput.exo_simul(1:initialconditions.nobs, i) = initialconditions{exonames{i}}.data;
|
|
end
|
|
end
|
|
|
|
|
|
if nargout>8
|
|
% Get function handles to the dynamic model routines.
|
|
dynamic_resid = str2func([DynareModel.fname,'.sparse.dynamic_resid']);
|
|
dynamic_g1 = str2func([DynareModel.fname,'.sparse.dynamic_g1']);
|
|
% initialization of vector y.
|
|
y = NaN(3*DynareModel.endo_nbr,1);
|
|
end
|