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Consider measurement error for uncertainty bands in Bayesian estimation

time-shift
Johannes Pfeifer 2016-06-18 14:33:11 +02:00 committed by Stéphane Adjemian (Hermes)
parent daa9d15c75
commit 7050cab9c9
3 changed files with 59 additions and 5 deletions
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5
matlab/forcst2.m
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@ -1,7 +1,8 @@
function yf=forcst2(y0,horizon,dr,n)
% function yf=forcst2(y0,horizon,dr,n)
%
% computes forecasts based on first order model solution, given shocks drawn from the shock distribution
% 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
@ -11,7 +12,7 @@ function yf=forcst2(y0,horizon,dr,n)
% Outputs:
% - yf [horizon+ykmin_ by endo_nbr by n] array of forecasts
%
% Copyright (C) 2008-2015 Dynare Team
% Copyright (C) 2008-2016 Dynare Team
%
% This file is part of Dynare.
%

25
matlab/prior_posterior_statistics.m
View File

@ -107,6 +107,9 @@ 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));
if ~isequal(M_.H,0)
MAX_nforc_ME = min(B,ceil(MaxNumberOfBytes/((size(options_.varobs,1))*(horizon+maxlag))/8));
end
end
MAX_momentsno = min(B,ceil(MaxNumberOfBytes/(get_moments_size(options_)*8)));
@ -126,7 +129,7 @@ for i=1:nvar
end
end
n_variables_to_fill=11;
n_variables_to_fill=12;
irun = ones(n_variables_to_fill,1);
ifil = zeros(n_variables_to_fill,1);
@ -163,6 +166,9 @@ if horizon
localVars.i_last_obs=i_last_obs;
localVars.MAX_nforc1=MAX_nforc1;
localVars.MAX_nforc2=MAX_nforc2;
if ~isequal(M_.H,0)
localVars.MAX_nforc_ME = MAX_nforc_ME;
end
end
localVars.exo_nbr=exo_nbr;
localVars.maxlag=maxlag;
@ -224,6 +230,10 @@ else
ifil(6,j+1) =ifil(6,j)+nfiles;
nfiles = ceil(nBlockPerCPU(j)/MAX_nforc2);
ifil(7,j+1) =ifil(7,j)+nfiles;
if ~isequal(M_.H,0)
nfiles = ceil(nBlockPerCPU(j)/MAX_nforc_ME);
ifil(12,j+1) =ifil(12,j)+nfiles;
end
end
if options_.filter_covariance
nfiles = ceil(nBlockPerCPU(j)/MAX_filter_covariance);
@ -318,6 +328,19 @@ if options_.forecast
pm3(endo_nbr,horizon,ifil(7),B,'Forecasted variables (point)',...
'',varlist,M_.endo_names_tex,M_.endo_names,...
varlist,'PointForecast',DirectoryName,'_forc_point');
if ~isequal(M_.H,0)
texnames=[];
for obs_iter=1:length(options_.varobs)
obs_names{obs_iter,1}=M_.endo_names(strmatch(options_.varobs{obs_iter},M_.endo_names,'exact'),:);
texnames{obs_iter,1}=M_.endo_names_tex(strmatch(options_.varobs{obs_iter},M_.endo_names,'exact'),:);
end
obs_names=char(obs_names);
texnames=char(texnames);
varlist_forecast_ME=intersect(options_.varobs,varlist);
pm3(meas_err_nbr,horizon,ifil(12),B,'Forecasted variables (point) with ME',...
'',char(varlist_forecast_ME),texnames,obs_names,...
char(varlist_forecast_ME),'PointForecastME',DirectoryName,'_forc_point_ME')
end
end
if options_.filter_covariance

34
matlab/prior_posterior_statistics_core.m
View File

@ -16,6 +16,7 @@ function myoutput=prior_posterior_statistics_core(myinputs,fpar,B,whoiam, ThisMa
% _param;
% _forc_mean;
% _forc_point;
% _forc_point_ME;
% _filter_covar;
% _trend_coeff;
% _smoothed_trend;
@ -74,6 +75,9 @@ 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;
@ -137,6 +141,7 @@ if RemoteFlag==1,
OutputFileName_param = {};
OutputFileName_forc_mean = {};
OutputFileName_forc_point = {};
OutputFileName_forc_point_ME = {};
OutputFileName_filter_covar ={};
OutputFileName_trend_coeff = {};
OutputFileName_smoothed_trend = {};
@ -155,6 +160,9 @@ if run_smoother
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
@ -300,6 +308,16 @@ for b=fpar:B
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;
@ -312,7 +330,7 @@ for b=fpar:B
stock_ys(irun(5),:) = SteadyState';
irun = irun + ones(11,1);
irun = irun + ones(12,1);
if run_smoother && (irun(1) > MAX_nsmoo || b == B),
@ -433,7 +451,18 @@ for b=fpar:B
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
dyn_waitbar((b-fpar+1)/(B-fpar+1),h);
end
@ -447,6 +476,7 @@ if RemoteFlag==1,
OutputFileName_param;
OutputFileName_forc_mean;
OutputFileName_forc_point;
OutputFileName_forc_point_ME;
OutputFileName_filter_covar;
OutputFileName_trend_coeff;
OutputFileName_smoothed_trend;