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function PosteriorFilterSmootherAndForecast(Y,gend, type,data_index)
% function PosteriorFilterSmootherAndForecast(Y,gend, type)
% Computes posterior filter smoother and forecasts
%
% INPUTS
% Y: data
% gend: number of observations
% type: posterior
% prior
% gsa
%
% OUTPUTS
% none
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2005-2012 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 <http://www.gnu.org/licenses/>.
global options_ estim_params_ oo_ M_ bayestopt_
nvx = estim_params_.nvx;
nvn = estim_params_.nvn;
ncx = estim_params_.ncx;
ncn = estim_params_.ncn;
np = estim_params_.np ;
npar = nvx+nvn+ncx+ncn+np;
offset = npar-np;
naK = length(options_.filter_step_ahead);
%%
MaxNumberOfPlotPerFigure = 4;% The square root must be an integer!
MaxNumberOfBytes=options_.MaxNumberOfBytes;
endo_nbr=M_.endo_nbr;
exo_nbr=M_.exo_nbr;
nvobs = size(options_.varobs,1);
nn = sqrt(MaxNumberOfPlotPerFigure);
iendo = 1:endo_nbr;
i_last_obs = gend+(1-M_.maximum_endo_lag:0);
horizon = options_.forecast;
maxlag = M_.maximum_endo_lag;
%%
CheckPath('Plots/',M_.dname);
DirectoryName = CheckPath('metropolis',M_.dname);
load([ DirectoryName '/' M_.fname '_mh_history.mat'])
FirstMhFile = record.KeepedDraws.FirstMhFile;
FirstLine = record.KeepedDraws.FirstLine;
TotalNumberOfMhFiles = sum(record.MhDraws(:,2)); LastMhFile = TotalNumberOfMhFiles;
TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
clear record;
B = min(1200, round(0.25*NumberOfDraws));
B = 200;
%%
MAX_nruns = min(B,ceil(options_.MaxNumberOfBytes/(npar+2)/8));
MAX_nsmoo = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*gend)/8));
MAX_ninno = min(B,ceil(MaxNumberOfBytes/(exo_nbr*gend)/8));
MAX_nerro = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)*gend)/8));
if naK
MAX_naK = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)* ...
length(options_.filter_step_ahead)*gend)/8));
end
if horizon
MAX_nforc1 = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*(horizon+maxlag))/8));
MAX_nforc2 = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*(horizon+maxlag))/ ...
8));
IdObs = bayestopt_.mfys;
end
%%
varlist = options_.varlist;
if isempty(varlist)
varlist = M_.endo_names;
SelecVariables = transpose(1:M_.endo_nbr);
nvar = M_.endo_nbr;
else
nvar = size(varlist,1);
SelecVariables = [];
for i=1:nvar
if ~isempty(strmatch(varlist(i,:),M_.endo_names,'exact'))
SelecVariables = [SelecVariables;strmatch(varlist(i,:),M_.endo_names,'exact')];
end
end
end
irun1 = 1;
irun2 = 1;
irun3 = 1;
irun4 = 1;
irun5 = 1;
irun6 = 1;
irun7 = 1;
ifil1 = 0;
ifil2 = 0;
ifil3 = 0;
ifil4 = 0;
ifil5 = 0;
ifil6 = 0;
ifil7 = 0;
h = waitbar(0,'Bayesian smoother...');
stock_param = zeros(MAX_nruns, npar);
stock_logpo = zeros(MAX_nruns,1);
stock_ys = zeros(MAX_nruns,endo_nbr);
if options_.smoother
stock_smooth = zeros(endo_nbr,gend,MAX_nsmoo);
stock_innov = zeros(exo_nbr,gend,B);
stock_error = zeros(nvobs,gend,MAX_nerro);
end
if options_.filter_step_ahead
stock_filter = zeros(naK,endo_nbr,gend+options_.filter_step_ahead(end),MAX_naK);
end
if options_.forecast
stock_forcst_mean = zeros(endo_nbr,horizon+maxlag,MAX_nforc1);
stock_forcst_total = zeros(endo_nbr,horizon+maxlag,MAX_nforc2);
end
for b=1:B
%deep = GetOneDraw(NumberOfDraws,FirstMhFile,LastMhFile,FirstLine,MAX_nruns,DirectoryName);
[deep, logpo] = GetOneDraw(type);
M_ = set_all_parameters(deep,estim_params_,M_);
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = ...
DsgeSmoother(deep,gend,Y,data_index);
if options_.loglinear
stock_smooth(dr.order_var,:,irun1) = alphahat(1:endo_nbr,:)+ ...
repmat(log(dr.ys(dr.order_var)),1,gend);
else
stock_smooth(dr.order_var,:,irun1) = alphahat(1:endo_nbr,:)+ ...
repmat(dr.ys(dr.order_var),1,gend);
end
if nvx
stock_innov(:,:,irun2) = etahat;
end
if nvn
stock_error(:,:,irun3) = epsilonhat;
end
if naK
stock_filter(:,dr.order_var,:,irun4) = aK(options_.filter_step_ahead,1:endo_nbr,:);
end
stock_param(irun5,:) = deep;
stock_logpo(irun5,1) = logpo;
stock_ys(irun5,:) = SteadyState';
if horizon
yyyy = alphahat(iendo,i_last_obs);
yf = forcst2a(yyyy,dr,zeros(horizon,exo_nbr));
if options_.prefilter == 1
yf(:,IdObs) = yf(:,IdObs)+repmat(bayestopt_.mean_varobs', ...
horizon+maxlag,1);
end
yf(:,IdObs) = yf(:,IdObs)+(gend+[1-maxlag:horizon]')*trend_coeff';
if options_.loglinear == 1
yf = yf+repmat(log(SteadyState'),horizon+maxlag,1);
% yf = exp(yf);
else
yf = yf+repmat(SteadyState',horizon+maxlag,1);
end
yf1 = forcst2(yyyy,horizon,dr,1);
if options_.prefilter == 1
yf1(:,IdObs,:) = yf1(:,IdObs,:)+ ...
repmat(bayestopt_.mean_varobs',[horizon+maxlag,1,1]);
end
yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat((gend+[1-maxlag:horizon]')* ...
trend_coeff',[1,1,1]);
if options_.loglinear == 1
yf1 = yf1 + repmat(log(SteadyState'),[horizon+maxlag,1,1]);
% yf1 = exp(yf1);
else
yf1 = yf1 + repmat(SteadyState',[horizon+maxlag,1,1]);
end
stock_forcst_mean(:,:,irun6) = yf';
stock_forcst_total(:,:,irun7) = yf1';
end
irun1 = irun1 + 1;
irun2 = irun2 + 1;
irun3 = irun3 + 1;
irun4 = irun4 + 1;
irun5 = irun5 + 1;
irun6 = irun6 + 1;
irun7 = irun7 + 1;
if irun1 > MAX_nsmoo || b == B
stock = stock_smooth(:,:,1:irun1-1);
ifil1 = ifil1 + 1;
save([DirectoryName '/' M_.fname '_smooth' int2str(ifil1) '.mat'],'stock');
irun1 = 1;
end
if nvx && (irun2 > MAX_ninno || b == B)
stock = stock_innov(:,:,1:irun2-1);
ifil2 = ifil2 + 1;
save([DirectoryName '/' M_.fname '_inno' int2str(ifil2) '.mat'],'stock');
irun2 = 1;
end
if nvn && (irun3 > MAX_error || b == B)
stock = stock_error(:,:,1:irun3-1);
ifil3 = ifil3 + 1;
save([DirectoryName '/' M_.fname '_error' int2str(ifil3) '.mat'],'stock');
irun3 = 1;
end
if naK && (irun4 > MAX_naK || b == B)
stock = stock_filter(:,:,:,1:irun4-1);
ifil4 = ifil4 + 1;
save([DirectoryName '/' M_.fname '_filter' int2str(ifil4) '.mat'],'stock');
irun4 = 1;
end
if irun5 > MAX_nruns || b == B
stock = stock_param(1:irun5-1,:);
ifil5 = ifil5 + 1;
save([DirectoryName '/' M_.fname '_param' int2str(ifil5) '.mat'],'stock','stock_logpo','stock_ys');
irun5 = 1;
end
if horizon && (irun6 > MAX_nforc1 || b == B)
stock = stock_forcst_mean(:,:,1:irun6-1);
ifil6 = ifil6 + 1;
save([DirectoryName '/' M_.fname '_forc_mean' int2str(ifil6) '.mat'],'stock');
irun6 = 1;
end
if horizon && (irun7 > MAX_nforc2 || b == B)
stock = stock_forcst_total(:,:,1:irun7-1);
ifil7 = ifil7 + 1;
save([DirectoryName '/' M_.fname '_forc_total' int2str(ifil7) '.mat'],'stock');
irun7 = 1;
end
waitbar(b/B,h);
end
close(h)
stock_gend=gend;
stock_data=Y;
save([DirectoryName '/' M_.fname '_data.mat'],'stock_gend','stock_data');
if options_.smoother
pm3(endo_nbr,gend,ifil1,B,'Smoothed variables',...
M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
'names2','smooth',[M_.fname '/metropolis'],'_smooth')
end
if options_.forecast
pm3(endo_nbr,horizon+maxlag,ifil6,B,'Forecasted variables (mean)',...
M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
'names2','smooth',[M_.fname '/metropolis'],'_forc_mean')
pm3(endo_nbr,horizon+maxlag,ifil6,B,'Forecasted variables (total)',...
M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
'names2','smooth',[M_.fname '/metropolis'],'_forc_total')
end
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