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Make imcforecast.m account for presence of trends

time-shift
Johannes Pfeifer 2016-04-01 11:19:58 +02:00
parent 450dd465fd
commit 1d13a5dd02
1 changed files with 39 additions and 17 deletions
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56
matlab/imcforecast.m
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@ -68,6 +68,9 @@ else
end
if estimated_model
if options_.prefilter
error('imcforecast:: Conditional forecasting does not support the prefiltering option')
end
if ischar(options_cond_fcst.parameter_set)
switch options_cond_fcst.parameter_set
case 'posterior_mode'
@ -107,18 +110,37 @@ if estimated_model
data_index = dataset_info.missing.aindex;
gend = dataset_.nobs;
missing_value = dataset_info.missing.state;
[atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(xparam,gend,data,data_index,missing_value);
trend = repmat(ys,1,options_cond_fcst.periods+1);
for i=1:M_.endo_nbr
j = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,'exact');
if ~isempty(j)
trend(i,:) = trend(i,:)+trend_coeff(j)*(gend+(0:options_cond_fcst.periods));
[atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff,aK,T,R,P,PK,decomp,trend_addition] = DsgeSmoother(xparam,gend,data,data_index,missing_value);
%get constant part
if options_.noconstant
constant = zeros(size(ys,1),options_cond_fcst.periods+1);
else
if options_.loglinear
constant = repmat(log(ys),1,options_cond_fcst.periods+1);
else
constant = repmat(ys,1,options_cond_fcst.periods+1);
end
end
trend = trend(oo_.dr.order_var,:);
%get trend part (which also takes care of prefiltering); needs to
%include the last period
if bayestopt_.with_trend == 1
[trend_addition] =compute_trend_coefficients(M_,options_,size(bayestopt_.smoother_mf,1),gend+options_cond_fcst.periods);
trend_addition = trend_addition(:,gend:end);
else
trend_addition=zeros(size(bayestopt_.smoother_mf,1),1+options_cond_fcst.periods);
end
% add trend to constant
for obs_iter=1:length(options_.varobs)
j = strmatch(options_.varobs{obs_iter},M_.endo_names,'exact');
constant(j,:) = constant(j,:)+trend_addition(obs_iter,:);
end
trend = constant(oo_.dr.order_var,:);
InitState(:,1) = atT(:,end);
else
graph_title='Calibration';
if ~isfield(oo_.dr,'kstate')
error('You need to call stoch_simul before conditional_forecast')
end
end
if isempty(options_.qz_criterium)
@ -178,7 +200,10 @@ end
% number of periods with constrained values
cL = size(constrained_paths,2);
constrained_paths = bsxfun(@minus,constrained_paths,trend(idx,1:cL));
%transform constrained periods into deviations from steady state; note that
%trend includes last actual data point and therefore we need to start in
%period 2
constrained_paths = bsxfun(@minus,constrained_paths,trend(idx,2:1+cL));
FORCS1_shocks = zeros(n1,cL,options_cond_fcst.replic);
@ -202,17 +227,15 @@ forecasts.controlled_variables = constrained_vars;
forecasts.instruments = options_cond_fcst.controlled_varexo;
for i = 1:EndoSize
eval(['forecasts.cond.Mean.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ' = mFORCS1(i,:)'';']);
forecasts.cond.Mean.(deblank(M_.endo_names(oo_.dr.order_var(i),:)))= mFORCS1(i,:)';
tmp = sort(squeeze(FORCS1(i,:,:))');
eval(['forecasts.cond.ci.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ...
' = [tmp(t1,:)'' ,tmp(t2,:)'' ]'';']);
forecasts.cond.ci.(deblank(M_.endo_names(oo_.dr.order_var(i),:))) = [tmp(t1,:)' ,tmp(t2,:)' ]';
end
for i = 1:n1
eval(['forecasts.controlled_exo_variables.Mean.' deblank(options_cond_fcst.controlled_varexo(i,:)) ' = mFORCS1_shocks(i,:)'';']);
forecasts.controlled_exo_variables.Mean.(deblank(options_cond_fcst.controlled_varexo(i,:))) = mFORCS1_shocks(i,:)';
tmp = sort(squeeze(FORCS1_shocks(i,:,:))');
eval(['forecasts.controlled_exo_variables.ci.' deblank(options_cond_fcst.controlled_varexo(i,:)) ...
' = [tmp(t1,:)'' ,tmp(t2,:)'' ]'';']);
forecasts.controlled_exo_variables.ci.(deblank(options_cond_fcst.controlled_varexo(i,:))) = [tmp(t1,:)' ,tmp(t2,:)' ]';
end
clear FORCS1 mFORCS1_shocks;
@ -231,10 +254,9 @@ end
mFORCS2 = mean(FORCS2,3);
for i = 1:EndoSize
eval(['forecasts.uncond.Mean.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ' = mFORCS2(i,:)'';']);
forecasts.uncond.Mean.(deblank(M_.endo_names(oo_.dr.order_var(i),:)))= mFORCS2(i,:)';
tmp = sort(squeeze(FORCS2(i,:,:))');
eval(['forecasts.uncond.ci.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ...
' = [tmp(t1,:)'' ,tmp(t2,:)'' ]'';']);
forecasts.uncond.ci.(deblank(M_.endo_names(oo_.dr.order_var(i),:))) = [tmp(t1,:)' ,tmp(t2,:)' ]';
end
forecasts.graph.title=graph_title;
forecasts.graph.fname=M_.fname;