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function myoutput=prior_posterior_statistics_core(myinputs,fpar,B,whoiam, ThisMatlab)
% PARALLEL CONTEXT
% Core functionality for prior_posterior.m function, which can be parallelized.
% See also the comment in posterior_sampler_core.m funtion.
%
% INPUTS
% See the comment in posterior_sampler_core.m funtion.
%
% OUTPUTS
% o myoutput [struc]
% Contained OutputFileName_smooth;
% _update;
% _inno;
% _error;
% _filter_step_ahead;
% _param;
% _forc_mean;
% _forc_point;
% _forc_point_ME;
% _filter_covar;
% _trend_coeff;
% _smoothed_trend;
% _smoothed_constant;
% _state_uncert;
%
% ALGORITHM
% Portion of prior_posterior.m function.
% This file is part of Dynare.
%
% SPECIAL REQUIREMENTS.
% None.
% Copyright © 2005-2023 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/>.
if nargin<4
whoiam=0;
end
% Reshape 'myinputs' for local computation.
% In order to avoid confusion in the name space, the instruction struct2local(myinputs) is replaced by:
M_=myinputs.M_;
oo_=myinputs.oo_;
options_=myinputs.options_;
estim_params_=myinputs.estim_params_;
bayestopt_=myinputs.bayestopt_;
type=myinputs.type;
run_smoother=myinputs.run_smoother;
filter_covariance=myinputs.filter_covariance;
smoothed_state_uncertainty=myinputs.smoothed_state_uncertainty;
gend=myinputs.gend;
Y=myinputs.Y;
data_index=myinputs.data_index;
missing_value=myinputs.missing_value;
varobs=myinputs.varobs;
mean_varobs=myinputs.mean_varobs;
irun=myinputs.irun;
endo_nbr=myinputs.endo_nbr;
nvn=myinputs.nvn;
naK=myinputs.naK;
horizon=myinputs.horizon;
iendo=myinputs.iendo;
IdObs=myinputs.IdObs; %index of observables
if horizon
i_last_obs=myinputs.i_last_obs;
MAX_nforc1=myinputs.MAX_nforc1;
MAX_nforc2=myinputs.MAX_nforc2;
if ~isequal(M_.H,0)
MAX_nforc_ME=myinputs.MAX_nforc_ME;
end
end
if naK
MAX_naK=myinputs.MAX_naK;
end
if filter_covariance
MAX_filter_covariance=myinputs.MAX_filter_covariance;
end
if smoothed_state_uncertainty
MAX_n_smoothed_state_uncertainty=myinputs.MAX_n_smoothed_state_uncertainty;
end
maxlag=myinputs.maxlag;
MAX_nsmoo=myinputs.MAX_nsmoo;
MAX_ninno=myinputs.MAX_ninno;
MAX_n_smoothed_constant=myinputs.MAX_n_smoothed_constant;
MAX_n_smoothed_trend=myinputs.MAX_n_smoothed_trend;
MAX_n_trend_coeff=myinputs.MAX_n_trend_coeff;
MAX_nerro = myinputs.MAX_nerro;
MAX_nruns=myinputs.MAX_nruns;
ifil=myinputs.ifil;
if ~strcmpi(type,'prior')
x=myinputs.x;
if strcmpi(type,'posterior')
logpost=myinputs.logpost;
end
else
Prior = dprior(bayestopt_,options_.prior_trunc);
options_ = select_qz_criterium_value(options_);
end
if whoiam
Parallel=myinputs.Parallel;
end
% DirectoryName = myinputs.DirectoryName;
if strcmpi(type,'posterior')
DirectoryName = CheckPath('metropolis',M_.dname);
elseif strcmpi(type,'gsa')
if options_.opt_gsa.pprior
DirectoryName = CheckPath(['gsa',filesep,'prior'],M_.dname);
else
DirectoryName = CheckPath(['gsa',filesep,'mc'],M_.dname);
end
elseif strcmpi(type,'prior')
DirectoryName = CheckPath('prior',M_.dname);
end
RemoteFlag = 0;
if whoiam
if Parallel(ThisMatlab).Local==0
RemoteFlag =1;
end
ifil=ifil(:,whoiam);
prct0={0,whoiam,Parallel(ThisMatlab)};
else
prct0=0;
end
h = dyn_waitbar(prct0,['Taking ',type,' subdraws...']);
if RemoteFlag==1
OutputFileName_smooth = {};
OutputFileName_update = {};
OutputFileName_inno = {};
OutputFileName_error = {};
OutputFileName_filter_step_ahead = {};
OutputFileName_param = {};
OutputFileName_forc_mean = {};
OutputFileName_forc_point = {};
OutputFileName_forc_point_ME = {};
OutputFileName_filter_covar ={};
OutputFileName_trend_coeff = {};
OutputFileName_smoothed_trend = {};
OutputFileName_smoothed_constant = {};
% OutputFileName_moments = {};
end
%initialize arrays
if run_smoother
stock_smooth=NaN(endo_nbr,gend,MAX_nsmoo);
stock_update=NaN(endo_nbr,gend,MAX_nsmoo);
stock_innov=NaN(M_.exo_nbr,gend,MAX_ninno);
stock_smoothed_constant=NaN(endo_nbr,gend,MAX_n_smoothed_constant);
stock_smoothed_trend=NaN(endo_nbr,gend,MAX_n_smoothed_trend);
stock_trend_coeff = zeros(endo_nbr,MAX_n_trend_coeff);
if horizon
stock_forcst_mean= NaN(endo_nbr,horizon,MAX_nforc1);
stock_forcst_point = NaN(endo_nbr,horizon,MAX_nforc2);
if ~isequal(M_.H,0)
stock_forcst_point_ME = NaN(length(varobs),horizon,MAX_nforc_ME);
end
end
end
if nvn
stock_error = NaN(length(varobs),gend,MAX_nerro);
end
if naK
stock_filter_step_ahead =NaN(length(options_.filter_step_ahead),endo_nbr,gend+max(options_.filter_step_ahead),MAX_naK);
end
stock_param = NaN(MAX_nruns, length(bayestopt_.p6));
stock_logpo = NaN(MAX_nruns, 1);
stock_ys = NaN(MAX_nruns, endo_nbr);
if filter_covariance
stock_filter_covariance = zeros(endo_nbr,endo_nbr,gend+1,MAX_filter_covariance);
end
if smoothed_state_uncertainty
stock_smoothed_uncert = zeros(endo_nbr,endo_nbr,gend,MAX_n_smoothed_state_uncertainty);
end
opts_local = options_;
for b=fpar:B
if strcmpi(type,'prior')
iter=1;
logpo=[];
while (isempty(logpo) || isinf(logpo)) && iter<1000
deep = Prior.draw();
logpo = evaluate_posterior_kernel(deep, M_, estim_params_, oo_, options_, bayestopt_);
iter=iter+1;
end
if iter==1000
fprintf('\nprior_posterior_statistics: unable to get a valid draw in 1000 tries. Giving up.')
end
else
deep = x(b,:);
if strcmpi(type,'posterior')
logpo = logpost(b);
else
logpo = evaluate_posterior_kernel(deep',M_,estim_params_,oo_,options_,bayestopt_);
end
end
M_ = set_all_parameters(deep,estim_params_,M_);
if run_smoother
[dr,info,M_.params] =compute_decision_rules(M_,opts_local,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
if ismember(info(1),[3,4])
opts_local.qz_criterium = 1 + (opts_local.qz_criterium-1)*10; %loosen tolerance, useful for big models where BK conditions were tested on restricted state space
[dr,info,M_.params] =compute_decision_rules(M_,opts_local,oo_);
end
if info(1)
message=get_error_message(info,opts_local);
fprintf('\nprior_posterior_statistics: One of the draws failed with the error:\n%s\n',message)
fprintf('prior_posterior_statistics: This should not happen. Please contact the developers.\n',message)
end
if options_.occbin.smoother.status
opts_local.occbin.simul.waitbar=0;
opts_local.occbin.smoother.waitbar = 0;
opts_local.occbin.smoother.linear_smoother=false; % speed-up
[alphahat,etahat,epsilonhat,alphatilde,SteadyState,trend_coeff,aK,~,~,P,~,~,trend_addition,state_uncertainty,oo_,bayestopt_] = ...
occbin.DSGE_smoother(deep,gend,Y,data_index,missing_value,M_,oo_,opts_local,bayestopt_,estim_params_);
if oo_.occbin.smoother.error_flag(1)
message=get_error_message(oo_.occbin.smoother.error_flag,opts_local);
fprintf('\nprior_posterior_statistics: One of the draws failed with the error:\n%s\n',message)
end
else
[alphahat,etahat,epsilonhat,alphatilde,SteadyState,trend_coeff,aK,~,~,P,~,~,trend_addition,state_uncertainty,oo_,bayestopt_] = ...
DsgeSmoother(deep,gend,Y,data_index,missing_value,M_,oo_,opts_local,bayestopt_,estim_params_);
end
stock_trend_coeff(options_.varobs_id,irun(9))=trend_coeff;
stock_smoothed_trend(IdObs,:,irun(11))=trend_addition;
if options_.loglinear %reads values from smoother results, which are in dr-order and put them into declaration order
constant_part=repmat(log(SteadyState(dr.order_var)),1,gend);
stock_smooth(dr.order_var,:,irun(1)) = alphahat(1:endo_nbr,:)+ ...
constant_part;
stock_update(dr.order_var,:,irun(1)) = alphatilde(1:endo_nbr,:)+ ...
constant_part;
else
constant_part=repmat(SteadyState(dr.order_var),1,gend);
stock_smooth(dr.order_var,:,irun(1)) = alphahat(1:endo_nbr,:)+ ...
constant_part;
stock_update(dr.order_var,:,irun(1)) = alphatilde(1:endo_nbr,:)+ ...
constant_part;
end
stock_smoothed_constant(dr.order_var,:,irun(10))=constant_part;
%% Compute constant for observables
if options_.prefilter == 1 %as mean is taken after log transformation, no distinction is needed here
constant_part=repmat(mean_varobs',1,gend);
elseif options_.prefilter == 0 && options_.loglinear %logged steady state must be used
constant_part=repmat(log(SteadyState(IdObs)),1,gend);
elseif options_.prefilter == 0 && ~options_.loglinear %unlogged steady state must be used
constant_part=repmat(SteadyState(IdObs),1,gend);
end
%add trend to observables
if options_.prefilter
%do correction for prefiltering for observed variables
if options_.loglinear
mean_correction=-repmat(log(SteadyState(IdObs)),1,gend)+constant_part;
else
mean_correction=-repmat(SteadyState(IdObs),1,gend)+constant_part;
end
stock_smoothed_constant(IdObs,:,irun(10))=stock_smoothed_constant(IdObs,:,irun(10))+mean_correction;
%smoothed variables are E_T(y_t) so no trend shift is required
stock_smooth(IdObs,:,irun(1))=stock_smooth(IdObs,:,irun(1))+trend_addition+mean_correction;
%updated variables are E_t(y_t) so no trend shift is required
stock_update(IdObs,:,irun(1))=stock_update(IdObs,:,irun(1))+trend_addition+mean_correction;
else
stock_smooth(IdObs,:,irun(1))=stock_smooth(IdObs,:,irun(1))+trend_addition;
stock_update(IdObs,:,irun(1))=stock_update(IdObs,:,irun(1))+trend_addition;
end
stock_innov(:,:,irun(2)) = etahat;
if nvn
stock_error(:,:,irun(3)) = epsilonhat;
end
if naK
%filtered variable E_t(y_t+k) requires to shift trend by k periods
%write variables into declaration order
if options_.loglinear %reads values from smoother results, which are in dr-order and put them into declaration order
constant_part=repmat(log(SteadyState(dr.order_var))',[length(options_.filter_step_ahead),1,gend+max(options_.filter_step_ahead)]);
else
constant_part=repmat(SteadyState(dr.order_var)',[length(options_.filter_step_ahead),1,gend+max(options_.filter_step_ahead)]);
end
stock_filter_step_ahead(:,dr.order_var,:,irun(4)) = aK(options_.filter_step_ahead,1:endo_nbr,:) + constant_part;
%now add trend to observables
for ii=1:length(options_.filter_step_ahead)
if options_.prefilter
zdim = size(stock_filter_step_ahead(ii,IdObs,:,irun(4)));
squeezed = reshape(stock_filter_step_ahead(ii,IdObs,:,irun(4)), [zdim(2:end) 1]);
stock_filter_step_ahead(ii,IdObs,:,irun(4)) = squeezed ...
+repmat(mean_correction(:,1),1,gend+max(options_.filter_step_ahead)) ... %constant correction
+[trend_addition repmat(trend_addition(:,end),1,max(options_.filter_step_ahead))+trend_coeff*[1:max(options_.filter_step_ahead)]]; %trend
else
zdim = size(stock_filter_step_ahead(ii,IdObs,:,irun(4)));
squeezed = reshape(stock_filter_step_ahead(ii,IdObs,:,irun(4)), [zdim(2:end) 1]);
stock_filter_step_ahead(ii,IdObs,:,irun(4)) = squeezed ...
+[trend_addition repmat(trend_addition(:,end),1,max(options_.filter_step_ahead))+trend_coeff*[1:max(options_.filter_step_ahead)]]; %trend
end
end
end
if horizon
yyyy = alphahat(iendo,i_last_obs);
yf = simulate_posterior_forecasts(yyyy,dr,horizon,false,M_.Sigma_e,1);
if options_.prefilter
% add mean
yf(:,IdObs) = yf(:,IdObs)+repmat(mean_varobs, ...
horizon+maxlag,1);
% add trend, taking into account that last point of sample is still included in forecasts and only cut off later
yf(:,IdObs) = yf(:,IdObs)+((options_.first_obs-1)+gend+[1-maxlag:horizon]')*trend_coeff'-...
repmat(mean(trend_coeff*[options_.first_obs:options_.first_obs+gend-1],2)',length(1-maxlag:horizon),1); %center trend
else
% add trend, taking into account that last point of sample is still included in forecasts and only cut off later
yf(:,IdObs) = yf(:,IdObs)+((options_.first_obs-1)+gend+[1-maxlag:horizon]')*trend_coeff';
end
if options_.loglinear
yf = yf+repmat(log(SteadyState'),horizon+maxlag,1);
else
yf = yf+repmat(SteadyState',horizon+maxlag,1);
end
yf1 = simulate_posterior_forecasts(yyyy,dr,horizon,false,M_.Sigma_e,1);
if options_.prefilter == 1
% add mean
yf1(:,IdObs,:) = yf1(:,IdObs,:)+ ...
repmat(mean_varobs,[horizon+maxlag,1,1]);
% add trend, taking into account that last point of sample is still included in forecasts and only cut off later
yf1(:,IdObs) = yf1(:,IdObs)+((options_.first_obs-1)+gend+[1-maxlag:horizon]')*trend_coeff'-...
repmat(mean(trend_coeff*[options_.first_obs:options_.first_obs+gend-1],2)',length(1-maxlag:horizon),1); %center trend
else
% add trend, taking into account that last point of sample is still included in forecasts and only cut off later
yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat(((options_.first_obs-1)+gend+[1-maxlag:horizon]')* ...
trend_coeff',[1,1,1]);
end
if options_.loglinear
yf1 = yf1 + repmat(log(SteadyState'),[horizon+maxlag,1,1]);
else
yf1 = yf1 + repmat(SteadyState',[horizon+maxlag,1,1]);
end
stock_forcst_mean(:,:,irun(6)) = yf(maxlag+1:end,:)';
stock_forcst_point(:,:,irun(7)) = yf1(maxlag+1:end,:)';
if ~isequal(M_.H,0)
ME_shocks=zeros(length(varobs),horizon);
i_exo_var = setdiff([1:length(varobs)],find(diag(M_.H) == 0));
nxs = length(i_exo_var);
chol_H = chol(M_.H(i_exo_var,i_exo_var));
if ~isempty(M_.H)
ME_shocks(i_exo_var,:) = chol_H*randn(nxs,horizon);
end
stock_forcst_point_ME(:,:,irun(12)) = yf1(maxlag+1:end,IdObs)'+ME_shocks;
end
end
if filter_covariance
stock_filter_covariance(dr.order_var,dr.order_var,:,irun(8)) = P;
end
if smoothed_state_uncertainty
stock_smoothed_uncert(dr.order_var,dr.order_var,:,irun(13)) = state_uncertainty;
end
else
[~,~,SteadyState] = dynare_resolve(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
end
stock_param(irun(5),:) = deep;
stock_logpo(irun(5),1) = logpo;
stock_ys(irun(5),:) = SteadyState';
irun = irun + ones(13,1);
if run_smoother && (irun(1) > MAX_nsmoo || b == B)
stock = stock_smooth(:,:,1:irun(1)-1);
ifil(1) = ifil(1) + 1;
save([DirectoryName '/' M_.fname '_smooth' int2str(ifil(1)) '.mat'],'stock');
stock = stock_update(:,:,1:irun(1)-1);
save([DirectoryName '/' M_.fname '_update' int2str(ifil(1)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_smooth = [OutputFileName_smooth; {[DirectoryName filesep], [M_.fname '_smooth' int2str(ifil(1)) '.mat']}];
OutputFileName_update = [OutputFileName_update; {[DirectoryName filesep], [M_.fname '_update' int2str(ifil(1)) '.mat']}];
end
irun(1) = 1;
end
if run_smoother && (irun(2) > MAX_ninno || b == B)
stock = stock_innov(:,:,1:irun(2)-1);
ifil(2) = ifil(2) + 1;
save([DirectoryName '/' M_.fname '_inno' int2str(ifil(2)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_inno = [OutputFileName_inno; {[DirectoryName filesep], [M_.fname '_inno' int2str(ifil(2)) '.mat']}];
end
irun(2) = 1;
end
if run_smoother && nvn && (irun(3) > MAX_nerro || b == B)
stock = stock_error(:,:,1:irun(3)-1);
ifil(3) = ifil(3) + 1;
save([DirectoryName '/' M_.fname '_error' int2str(ifil(3)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_error = [OutputFileName_error; {[DirectoryName filesep], [M_.fname '_error' int2str(ifil(3)) '.mat']}];
end
irun(3) = 1;
end
if run_smoother && naK && (irun(4) > MAX_naK || b == B)
stock = stock_filter_step_ahead(:,:,:,1:irun(4)-1);
ifil(4) = ifil(4) + 1;
save([DirectoryName '/' M_.fname '_filter_step_ahead' int2str(ifil(4)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_filter_step_ahead = [OutputFileName_filter_step_ahead; {[DirectoryName filesep], [M_.fname '_filter_step_ahead' int2str(ifil(4)) '.mat']}];
end
irun(4) = 1;
end
if irun(5) > MAX_nruns || b == B
stock = stock_param(1:irun(5)-1,:);
stock_logpo = stock_logpo(1:irun(5)-1);
stock_ys = stock_ys(1:irun(5)-1,:);
ifil(5) = ifil(5) + 1;
save([DirectoryName '/' M_.fname '_param' int2str(ifil(5)) '.mat'],'stock','stock_logpo','stock_ys');
if RemoteFlag==1
OutputFileName_param = [OutputFileName_param; {[DirectoryName filesep], [M_.fname '_param' int2str(ifil(5)) '.mat']}];
end
irun(5) = 1;
end
if run_smoother && horizon && (irun(6) > MAX_nforc1 || b == B)
stock = stock_forcst_mean(:,:,1:irun(6)-1);
ifil(6) = ifil(6) + 1;
save([DirectoryName '/' M_.fname '_forc_mean' int2str(ifil(6)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_forc_mean = [OutputFileName_forc_mean; {[DirectoryName filesep], [M_.fname '_forc_mean' int2str(ifil(6)) '.mat']}];
end
irun(6) = 1;
end
if run_smoother && horizon && (irun(7) > MAX_nforc2 || b == B)
stock = stock_forcst_point(:,:,1:irun(7)-1);
ifil(7) = ifil(7) + 1;
save([DirectoryName '/' M_.fname '_forc_point' int2str(ifil(7)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_forc_point = [OutputFileName_forc_point; {[DirectoryName filesep], [M_.fname '_forc_point' int2str(ifil(7)) '.mat']}];
end
irun(7) = 1;
end
if run_smoother && filter_covariance && (irun(8) > MAX_filter_covariance || b == B)
stock = stock_filter_covariance(:,:,:,1:irun(8)-1);
ifil(8) = ifil(8) + 1;
save([DirectoryName '/' M_.fname '_filter_covar' int2str(ifil(8)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_filter_covar = [OutputFileName_filter_covar; {[DirectoryName filesep], [M_.fname '_filter_covar' int2str(ifil(8)) '.mat']}];
end
irun(8) = 1;
end
irun_index=9;
if run_smoother && (irun(irun_index) > MAX_n_trend_coeff || b == B)
stock = stock_trend_coeff(:,1:irun(irun_index)-1);
ifil(irun_index) = ifil(irun_index) + 1;
save([DirectoryName '/' M_.fname '_trend_coeff' int2str(ifil(irun_index)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_trend_coeff = [OutputFileName_trend_coeff; {[DirectoryName filesep], [M_.fname '_trend_coeff' int2str(ifil(irun_index)) '.mat']}];
end
irun(irun_index) = 1;
end
irun_index=10;
if run_smoother && (irun(irun_index) > MAX_n_smoothed_constant || b == B)
stock = stock_smoothed_constant(:,:,1:irun(irun_index)-1);
ifil(irun_index) = ifil(irun_index) + 1;
save([DirectoryName '/' M_.fname '_smoothed_constant' int2str(ifil(irun_index)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_smoothed_constant = [OutputFileName_smoothed_constant; {[DirectoryName filesep], [M_.fname '_smoothed_constant' int2str(ifil(irun_index)) '.mat']}];
end
irun(irun_index) = 1;
end
irun_index=11;
if run_smoother && (irun(irun_index) > MAX_n_smoothed_trend || b == B)
stock = stock_smoothed_trend(:,:,1:irun(irun_index)-1);
ifil(irun_index) = ifil(irun_index) + 1;
save([DirectoryName '/' M_.fname '_smoothed_trend' int2str(ifil(irun_index)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_smoothed_trend = [OutputFileName_smoothed_trend; {[DirectoryName filesep], [M_.fname '_smoothed_trend' int2str(ifil(irun_index)) '.mat']}];
end
irun(irun_index) = 1;
end
irun_index=12;
if run_smoother && horizon && ~isequal(M_.H,0) && (irun(irun_index) > MAX_nforc_ME || b == B)
stock = stock_forcst_point_ME(:,:,1:irun(irun_index)-1);
ifil(irun_index) = ifil(irun_index) + 1;
save([DirectoryName '/' M_.fname '_forc_point_ME' int2str(ifil(irun_index)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_forc_point_ME = [OutputFileName_forc_point_ME; {[DirectoryName filesep], [M_.fname '_forc_point_ME' int2str(ifil(irun_index)) '.mat']}];
end
irun(irun_index) = 1;
end
irun_index=13;
if run_smoother && smoothed_state_uncertainty && (irun(irun_index) > MAX_n_smoothed_state_uncertainty || b == B)
stock = stock_smoothed_uncert(:,:,:,1:irun(irun_index)-1);
ifil(irun_index) = ifil(irun_index) + 1;
save([DirectoryName '/' M_.fname '_state_uncert' int2str(ifil(irun_index)) '.mat'],'stock');
if RemoteFlag==1
OutputFileName_state_uncert = [OutputFileName_state_uncert; {[DirectoryName filesep], [M_.fname '_state_uncert' int2str(ifil(irun_index)) '.mat']}];
end
irun(irun_index) = 1;
end
if mod(b-fpar+1, 5)==0
dyn_waitbar((b-fpar+1)/(B-fpar+1),h);
end
end
myoutput.ifil=ifil;
if RemoteFlag==1
myoutput.OutputFileName = [OutputFileName_smooth;
OutputFileName_update;
OutputFileName_inno;
OutputFileName_error;
OutputFileName_filter_step_ahead;
OutputFileName_param;
OutputFileName_forc_mean;
OutputFileName_forc_point;
OutputFileName_forc_point_ME;
OutputFileName_filter_covar;
OutputFileName_trend_coeff;
OutputFileName_smoothed_trend;
OutputFileName_smoothed_constant;
OutputFileName_state_uncert];
end
dyn_waitbar_close(h);
function yf=simulate_posterior_forecasts(y0,dr,horizon,stochastic_indicator,Sigma_e,n)
% function yf=forcst2(y0,horizon,dr,n)
%
% computes forecasts based on first order model solution, given shocks
% drawn from the shock distribution, but not including measurement error
% Inputs:
% - y0 [endo_nbr by maximum_endo_lag] matrix of starting values
% - dr [structure] structure with Dynare decision rules
% - horizon [scalar] number of forecast periods
% - stochastic_indicator [boolean] indicator whether to consider stochastic shocks
% - Sigma_e [integer] covariance matrix of shocks
% - n [scalar] number of repetitions
%
% Outputs:
% - yf [horizon+ykmin_ by endo_nbr by n] array of forecasts
if nargin< 4
stochastic_indicator=false;
n=1;
end
%select states
k2 = dr.inv_order_var(dr.state_var);
if stochastic_indicator
% eliminate shocks with 0 variance
i_exo_var = setdiff(1:length(Sigma_e),find(diag(Sigma_e) == 0));
nxs = length(i_exo_var);
chol_S = chol(Sigma_e(i_exo_var,i_exo_var));
if ~isempty(Sigma_e)
e = randn(nxs,n,horizon);
end
B1 = dr.ghu(:,i_exo_var)*chol_S';
end
endo_nbr=length(y0);
yf = zeros(endo_nbr,1+horizon,n);
yf(:,1,:,:) = repmat(y0,[1,1,n]);
for iter=1:horizon
if stochastic_indicator
yf(:,iter+1,:) = dr.ghx*squeeze(yf(k2,iter,:))+B1*squeeze(e(:,:,iter));
else
yf(:,iter+1,:) = dr.ghx*squeeze(yf(k2,iter,:));
end
end
yf(dr.order_var,:,:) = yf;
yf=permute(yf,[2 1 3]);
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