Factorize saving of smoother results
Also takes care of prefilter and loglinear option for calib_smoother Closes #803 and #804time-shift
Johannes Pfeifer
Johannes Pfeifer
parent
369d8dd7a4
commit
6f8ca74490
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@ -171,38 +171,7 @@ end
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if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && options_.mh_posterior_mode_estimation==0
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if options_.smoother == 1
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp,Trend] = DsgeSmoother(xparam1,gend,transpose(data),data_index,missing_value);
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oo_.Smoother.SteadyState = ys;
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oo_.Smoother.TrendCoeffs = trend_coeff;
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oo_.Smoother.Trend = Trend;
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if options_.filter_covariance
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oo_.Smoother.Variance = P;
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end
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i_endo = bayestopt_.smoother_saved_var_list;
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if options_.nk ~= 0
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oo_.FilteredVariablesKStepAhead = ...
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aK(options_.filter_step_ahead,i_endo,:);
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if ~isempty(PK)
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oo_.FilteredVariablesKStepAheadVariances = ...
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PK(options_.filter_step_ahead,i_endo,i_endo,:);
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end
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if ~isempty(decomp)
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oo_.FilteredVariablesShockDecomposition = ...
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decomp(options_.filter_step_ahead,i_endo,:,:);
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end
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end
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for i=bayestopt_.smoother_saved_var_list'
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i1 = dr.order_var(bayestopt_.smoother_var_list(i));
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eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ...
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' = atT(i,:)'';']);
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if options_.nk > 0
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eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ...
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' = squeeze(aK(1,i,2:end-(options_.nk-1)));']);
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end
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eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ' = updated_variables(i,:)'';']);
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end
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for i=1:M_.exo_nbr
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eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
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end
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[oo_]=write_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend);
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end
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return
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end
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@ -515,39 +484,41 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
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|| ~options_.smoother ) && options_.partial_information == 0 % to be fixed
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%% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variable
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp,Trend] = DsgeSmoother(xparam1,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state);
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oo_.Smoother.SteadyState = ys;
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oo_.Smoother.TrendCoeffs = trend_coeff;
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oo_.Smoother.Trend = Trend;
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oo_.Smoother.Variance = P;
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i_endo = bayestopt_.smoother_saved_var_list;
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if ~isempty(options_.nk) && options_.nk ~= 0 && (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file)))
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oo_.FilteredVariablesKStepAhead = aK(options_.filter_step_ahead, ...
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i_endo,:);
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if isfield(options_,'kalman_algo')
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if ~isempty(PK)
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oo_.FilteredVariablesKStepAheadVariances = ...
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PK(options_.filter_step_ahead,i_endo,i_endo,:);
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end
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if ~isempty(decomp)
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oo_.FilteredVariablesShockDecomposition = ...
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decomp(options_.filter_step_ahead,i_endo,:,:);
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end
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end
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end
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for i=bayestopt_.smoother_saved_var_list'
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i1 = dr.order_var(bayestopt_.smoother_var_list(i));
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eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = ' ...
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'atT(i,:)'';']);
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if ~isempty(options_.nk) && options_.nk > 0 && ~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file))
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eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ...
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' = squeeze(aK(1,i,2:end-(options_.nk-1)));']);
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end
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eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ...
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' = updated_variables(i,:)'';']);
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end
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for i=1:M_.exo_nbr
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eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
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end
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[oo_,yf]=write_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend);
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% oo_.Smoother.SteadyState = ys;
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% oo_.Smoother.TrendCoeffs = trend_coeff;
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% oo_.Smoother.Trend = Trend;
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% oo_.Smoother.Variance = P;
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% i_endo = bayestopt_.smoother_saved_var_list;
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% if ~isempty(options_.nk) && options_.nk ~= 0 && (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file)))
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% oo_.FilteredVariablesKStepAhead = aK(options_.filter_step_ahead, ...
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% i_endo,:);
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% if isfield(options_,'kalman_algo')
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% if ~isempty(PK)
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% oo_.FilteredVariablesKStepAheadVariances = ...
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% PK(options_.filter_step_ahead,i_endo,i_endo,:);
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% end
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% if ~isempty(decomp)
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% oo_.FilteredVariablesShockDecomposition = ...
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% decomp(options_.filter_step_ahead,i_endo,:,:);
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% end
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% end
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% end
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% for i=bayestopt_.smoother_saved_var_list'
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% i1 = dr.order_var(bayestopt_.smoother_var_list(i));
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% eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = ' ...
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% 'atT(i,:)'';']);
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% if ~isempty(options_.nk) && options_.nk > 0 && ~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file))
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% eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ...
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% ' = squeeze(aK(1,i,2:end-(options_.nk-1)));']);
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% end
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% eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ...
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% ' = updated_variables(i,:)'';']);
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% end
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% for i=1:M_.exo_nbr
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% eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
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% end
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if ~options_.nograph,
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[nbplt,nr,nc,lr,lc,nstar] = pltorg(M_.exo_nbr);
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if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
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@ -618,16 +589,16 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
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fclose(fidTeX);
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end
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end
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%%
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%% Smooth observational errors...
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%%
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if options_.prefilter == 1 %as mean is taken after log transformation, no distinction is needed here
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yf = atT(bayestopt_.mf,:)+repmat(dataset_info.descriptive.mean',1,gend)+Trend;
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elseif options_.loglinear == 1
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yf = atT(bayestopt_.mf,:)+repmat(log(ys(bayestopt_.mfys)),1,gend)+Trend;
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else
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yf = atT(bayestopt_.mf,:)+repmat(ys(bayestopt_.mfys),1,gend)+Trend;
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end
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% %%
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% %% Smooth observational errors...
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% %%
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% if options_.prefilter == 1 %as mean is taken after log transformation, no distinction is needed here
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% yf = atT(bayestopt_.mf,:)+repmat(dataset_info.descriptive.mean',1,gend)+Trend;
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% elseif options_.loglinear == 1
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% yf = atT(bayestopt_.mf,:)+repmat(log(ys(bayestopt_.mfys)),1,gend)+Trend;
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% else
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% yf = atT(bayestopt_.mf,:)+repmat(ys(bayestopt_.mfys),1,gend)+Trend;
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% end
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if nvn
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number_of_plots_to_draw = 0;
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index = [];
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@ -636,7 +607,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
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number_of_plots_to_draw = number_of_plots_to_draw + 1;
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index = cat(1,index,i);
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end
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eval(['oo_.SmoothedMeasurementErrors.' options_.varobs{i} ' = measurement_error(i,:)'';']);
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% eval(['oo_.SmoothedMeasurementErrors.' options_.varobs{i} ' = measurement_error(i,:)'';']);
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end
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if ~options_.nograph
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[nbplt,nr,nc,lr,lc,nstar] = pltorg(number_of_plots_to_draw);
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@ -87,35 +87,6 @@ if ischar(parameters)
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end
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end
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = ...
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp,Trend] = ...
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DsgeSmoother(parameters,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state);
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oo_.Smoother.SteadyState = ys;
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oo_.Smoother.TrendCoeffs = trend_coeff;
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if options_.filter_covariance
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oo_.Smoother.Variance = P;
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end
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i_endo = bayestopt_.smoother_saved_var_list;
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if options_.nk ~= 0
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oo_.FilteredVariablesKStepAhead = ...
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aK(options_.filter_step_ahead,i_endo,:);
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if ~isempty(PK)
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oo_.FilteredVariablesKStepAheadVariances = ...
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PK(options_.filter_step_ahead,i_endo,i_endo,:);
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end
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if ~isempty(decomp)
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oo_.FilteredVariablesShockDecomposition = ...
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decomp(options_.filter_step_ahead,i_endo,:,:);
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end
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end
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for i=bayestopt_.smoother_saved_var_list'
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i1 = oo_.dr.order_var(bayestopt_.smoother_var_list(i));
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eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = atT(i,:)'';']);
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if options_.nk>0
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eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ' = squeeze(aK(1,i,:));']);
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end
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eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ' = updated_variables(i,:)'';']);
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end
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for i=1:M_.exo_nbr
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eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
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end
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[oo_]=write_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend);
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@ -0,0 +1,147 @@
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function [oo_, yf]=write_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend)
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% oo_=write_smoother_results(M_,oo_,options_,bayestopt_,dataset_,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend)
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% Writes the smoother results into respective fields in oo_
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%
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% Inputs:
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% M_ [structure] storing the model information
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% oo_ [structure] storing the results
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% options_ [structure] storing the options
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% bayestopt_ [structure] storing information about priors
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% dataset_ [structure] storing the dataset
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% atT [double] (m*T) matrix, smoothed endogenous variables.
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% innov [double] (r*T) matrix, smoothed structural shocks (r>n is the umber of shocks).
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% measurement_error [double] (n*T) matrix, smoothed measurement errors.
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% updated_variables [double] (m*T) matrix, one step ahead filtered (endogenous) variables.
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% ys [double] (m*1) vector specifying the steady state level of each endogenous variable.
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% trend_coeff [double] (n*1) vector, parameters specifying the slope of the trend associated to each observed variable.
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% aK [double] (K,n,T+K) array, k (k=1,...,K) steps ahead filtered (endogenous) variables.
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% P [3D array] of one-step ahead forecast error variance
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% matrices
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% PK [4D array] of k-step ahead forecast error variance
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% matrices (meaningless for periods 1:d)
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% decomp
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% Trend [double] [nvarobs*T] matrix of trends in observables
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%
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% Outputs:
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% oo_ [structure] storing the results
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% yf [double] (nvarobs*T) matrix storing the smoothed observed variables
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%
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% Notes: first all smoothed variables are saved without trend and constant.
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% Then trend and constant are added for the observed variables.
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%
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% Copyright (C) 2014 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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gend=dataset_.nobs;
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if nargin<16
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Trend=zeros(options_.number_of_observed_variables,gend);
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end
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%% write variable steady state
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oo_.Smoother.SteadyState = ys;
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%% write trend coefficients and trend
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oo_.Smoother.TrendCoeffs = trend_coeff; %are in order of options_.varobs
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if ~isempty(Trend)
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for var_iter=1:size(options_.varobs,2)
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oo_.Smoother.Trend.(deblank(options_.varobs{1,var_iter})) = Trend(var_iter,:)';
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end
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end
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%% Compute constant
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if options_.prefilter == 1 %as mean is taken after log transformation, no distinction is needed here
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constant_part=repmat(dataset_info.descriptive.mean',1,gend);
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elseif options_.prefilter == 0 && options_.loglinear == 1 %logged steady state must be used
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constant_part=repmat(log(ys(bayestopt_.mfys)),1,gend);
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elseif options_.prefilter == 0 && options_.loglinear == 0 %unlogged steady state must be used
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constant_part=repmat(ys(bayestopt_.mfys),1,gend);
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end
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%% get observed variables including trend and constant
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trend_constant_observables=constant_part+Trend;
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yf = atT(bayestopt_.mf,:)+trend_constant_observables;
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if options_.nk > 0
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%filtered variable E_t(y_t+k) requires to shift trend by k periods
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filter_steps_required=union(1,options_.filter_step_ahead); % 1 is required for standard filtered variables
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for filter_iter=1:length(filter_steps_required)
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filter_step=filter_steps_required(filter_iter);
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trend_constant_observables_filtered.(['filter_ahead_' num2str(filter_step)])=constant_part+[Trend+repmat(filter_step*trend_coeff,1,gend)];
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end
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end
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%% write smoother variance
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if options_.filter_covariance
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oo_.Smoother.Variance = P;
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end
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%get indicees of smoothed variables
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i_endo = bayestopt_.smoother_saved_var_list;
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if ~isempty(options_.nk) && options_.nk ~= 0 && (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file)))
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%write deviations from steady state, add constant for observables later
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oo_.FilteredVariablesKStepAhead = aK(options_.filter_step_ahead,i_endo,:);
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if ~isempty(PK) %get K-step ahead variances
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oo_.FilteredVariablesKStepAheadVariances = ...
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PK(options_.filter_step_ahead,i_endo,i_endo,:);
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end
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if ~isempty(decomp) %get decomposition
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oo_.FilteredVariablesShockDecomposition = ...
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decomp(options_.filter_step_ahead,i_endo,:,:);
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end
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end
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for i=bayestopt_.smoother_saved_var_list'
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i1 = oo_.dr.order_var(bayestopt_.smoother_var_list(i)); %get indices of smoothed variables in name vector
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oo_.SmoothedVariables.(deblank(M_.endo_names(i1,:)))=atT(i,:)';
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if options_.nk > 0 && ~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file))
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oo_.FilteredVariables.(deblank(M_.endo_names(i1,:)))=squeeze(aK(1,i,2:end-(options_.nk-1)));
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end
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oo_.UpdatedVariables.(deblank(M_.endo_names(i1,:)))=updated_variables(i,:)';
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end
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%% Add trend and constant for observed variables
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for pos_iter=1:length(bayestopt_.mf)
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oo_.Smoother.Constant.(deblank(M_.endo_names(bayestopt_.mfys(pos_iter),:)))=constant_part(pos_iter,:);
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oo_.SmoothedVariables.(deblank(M_.endo_names(bayestopt_.mfys(pos_iter),:)))=yf(pos_iter,:)';
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if options_.nk > 0 && ~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file))
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%filtered variable E_t(y_t+1) requires to shift trend by 1 period
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oo_.FilteredVariables.(deblank(M_.endo_names(bayestopt_.mfys(pos_iter),:)))=...
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squeeze(aK(1,bayestopt_.mf(pos_iter),2:end-(options_.nk-1)))...
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+trend_constant_observables_filtered.filter_ahead_1(pos_iter,:)';
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for filter_iter=1:length(options_.filter_step_ahead)
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filter_step=options_.filter_step_ahead(filter_iter);
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oo_.FilteredVariablesKStepAhead(filter_iter,bayestopt_.mf(pos_iter),1+filter_step:end-(max(options_.filter_step_ahead)-filter_step)) = ...
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squeeze(aK(filter_step,bayestopt_.mf(pos_iter),1+filter_step:end-(max(options_.filter_step_ahead)-filter_step)))...
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+trend_constant_observables_filtered.(['filter_ahead_' num2str(filter_step)])(pos_iter,:)';
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end
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end
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%updated variables are E_t(y_t) so no trend shift is required
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oo_.UpdatedVariables.(deblank(M_.endo_names(bayestopt_.mfys(pos_iter),:)))=...
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updated_variables(bayestopt_.mf(pos_iter),:)'+trend_constant_observables(pos_iter,:)';
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end
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||||
|
||||
%% get smoothed shocks
|
||||
for i=1:M_.exo_nbr
|
||||
oo_.SmoothedShocks.(deblank(M_.exo_names(i,:)))=innov(i,:)';
|
||||
end
|
||||
|
||||
%% Smoothed measurement errors
|
||||
if ~isequal(M_.H,0)
|
||||
% measurement_error_indices=find(diag(M_.H)~=0);
|
||||
for meas_error_iter=1:length(options_.varobs)
|
||||
oo_.SmoothedMeasurementErrors.(options_.varobs{meas_error_iter})= measurement_error(meas_error_iter,:)';
|
||||
end
|
||||
end
|
||||
Loading…
Reference in New Issue