irf_matching: adaptations for new interface
Willi Mutschler
parent
f14bbc73b1
commit
2f07fa2921
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@ -42,9 +42,21 @@ function display_comparison_moments_irfs(M_, options_mom_, data_moments, model_m
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if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
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titl = upper('Comparison of matched data IRFs and model IRFs');
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headers = {'IRF','Data','Model'};
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for jm = 1:size(M_.matched_irfs,1)
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labels{jm,1} = [M_.endo_names{M_.matched_irfs{jm,1}(1)} ' ' M_.exo_names{M_.matched_irfs{jm,1}(2)} ' (' num2str(M_.matched_irfs{jm,1}(3)) ')'];
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labels_TeX{jm,1} = [M_.endo_names_tex{M_.matched_irfs{jm,1}(1)} ' ' M_.exo_names_tex{M_.matched_irfs{jm,1}(2)} ' (' num2str(M_.matched_irfs{jm,1}(3)) ')'];
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idx = 1;
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for jj = 1:size(M_.matched_irfs,1)
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irf_varname = M_.matched_irfs{jj,1};
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irf_shockname = M_.matched_irfs{jj,2};
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% note that periods can span over multiple rows
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IRF_PERIODS = [];
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for kk = 1:size(M_.matched_irfs{jj,3},1)
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irf_periods = M_.matched_irfs{jj,3}{kk,1};
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IRF_PERIODS = [IRF_PERIODS; irf_periods(:)];
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end
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for hh = 1:length(IRF_PERIODS)
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labels{idx,1} = sprintf('%s %s (%u)',irf_varname,irf_shockname,IRF_PERIODS(hh));
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labels_TeX{idx,1} = sprintf('%s %s (%u)',M_.endo_names_tex{ismember(M_.endo_names,irf_varname)},M_.exo_names_tex{ismember(M_.exo_names,irf_shockname)},IRF_PERIODS(hh));
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idx = idx+1;
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end
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end
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else
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titl = ['Comparison of matched data moments and model moments (',options_mom_.mom.mom_method,')'];
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@ -1,5 +1,5 @@
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function graph_comparison_irfs(matched_irfs,irf_model_varobs,varobs_id,irf_horizon,relative_irf,endo_names,exo_names,exo_names_tex,dname,fname,graph_format,TeX,nodisplay,figures_textwidth)
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% graph_comparison_irfs(matched_irfs,irf_model_varobs,varobs_id,irf_horizon,relative_irf,endo_names,exo_names,exo_names_tex,dname,fname,graph_format,TeX,nodisplay,figures_textwidth)
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function graph_comparison_irfs(matched_irfs,irf_model_varobs,varobs_id,irf_horizon,relative_irf,endo_names,endo_names_tex,exo_names,exo_names_tex,dname,fname,graph_format,TeX,nodisplay,figures_textwidth)
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% graph_comparison_irfs(matched_irfs,irf_model_varobs,varobs_id,irf_horizon,relative_irf,endo_names,endo_names_tex,exo_names,exo_names_tex,dname,fname,graph_format,TeX,nodisplay,figures_textwidth)
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% -------------------------------------------------------------------------
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% Plots and saves to disk the comparison of the selected data IRFs and corresponding model IRfs
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% -------------------------------------------------------------------------
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@ -10,6 +10,7 @@ function graph_comparison_irfs(matched_irfs,irf_model_varobs,varobs_id,irf_horiz
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% irf_horizon: [scalar] maximum horizon of IRFs
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% relative_irf: [boolean] if true, plots normalized IRFs
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% endo_names: [cell] names of endogenous variables
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% endo_names_tex: [cell] names of endogenous variables in latex
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% exo_names: [cell] names of exogenous variables
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% exo_names_tex: [cell] names of exogenous variables in latex
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% dname: [string] name of the directory where to save the graphs
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@ -59,40 +60,48 @@ if TeX && any(strcmp('eps',cellstr(graph_format)))
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fprintf(fid_TeX,['%% ' datestr(now,0) '\n']);
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fprintf(fid_TeX,' \n');
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end
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shock_entries = cellfun(@(x) x(2), matched_irfs(:, 1));
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unique_shock_entries = unique(shock_entries);
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unique_shock_entries = unique(matched_irfs(:, 2));
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colDarkGrey = [0.3, 0.3, 0.3]; % dark grey
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for jexo = unique_shock_entries'
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entries_data_irfs = find(cellfun(@(x) x(2) == jexo, matched_irfs(:, 1)));
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unique_variables = unique(cellfun(@(x) x(1), matched_irfs(entries_data_irfs,1)));
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for jexo = unique_shock_entries' % loop over cell with shock names
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unique_variables = unique(matched_irfs(ismember(matched_irfs(:, 2),jexo), 1));
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[nbplt,nr,nc,lr,lc,nstar] = pltorg(length(unique_variables));
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fig = 0;
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for jvar = 1:length(unique_variables)
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% get data points, note that periods and values can span over multiple rows
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jj = ismember(matched_irfs(:,1), unique_variables(jvar)) & ismember(matched_irfs(:,2), jexo);
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IRF_PERIODS = []; IRF_VALUES = [];
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for kk = 1:size(matched_irfs{jj,3},1)
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irf_periods = matched_irfs{jj,3}{kk,1};
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irf_values = matched_irfs{jj,3}{kk,2};
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if length(irf_values)==1
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irf_values = repmat(irf_values,length(irf_periods),1);
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end
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IRF_PERIODS = [IRF_PERIODS; irf_periods(:)];
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IRF_VALUES = [IRF_VALUES; irf_values(:)];
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end
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if jvar==1 || ~( (fig-1)*nstar<jvar && jvar<=fig*nstar )
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fig = fig+1;
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fig_irf = dyn_figure(nodisplay,'Name',['IRF matching shock to ' exo_names{jexo} ' figure ' int2str(fig)]);
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fig_irf = dyn_figure(nodisplay,'Name',['IRF matching shock to ' jexo{:} ' figure ' int2str(fig)]);
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end
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plt = jvar-(fig-1)*nstar;
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data_irf_rows = find(cellfun(@(x) x(1) == unique_variables(jvar) && x(2) == jexo, matched_irfs(:, 1)));
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data_irf_periods = cellfun(@(x) x(3), matched_irfs(data_irf_rows,1));
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data_irf_values = cell2mat(matched_irfs(data_irf_rows,2));
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plt = jvar-(fig-1)*nstar;
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if nbplt>1 && fig==nbplt
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subplot(lr,lc,plt);
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else
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subplot(nr,nc,plt);
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end
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plt_data = plot(data_irf_periods,data_irf_values,'h', 'MarkerEdgeColor',colDarkGrey,'MarkerFaceColor',colDarkGrey,'MarkerSize',8);
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plt_data = plot(IRF_PERIODS,IRF_VALUES,'h', 'MarkerEdgeColor',colDarkGrey,'MarkerFaceColor',colDarkGrey,'MarkerSize',8);
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hold on
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plt_model = plot(1:irf_horizon, irf_model_varobs(:,varobs_id==unique_variables(jvar),jexo),'-k','linewidth',2);
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plt_model = plot(1:irf_horizon, irf_model_varobs(:,varobs_id==find(ismember(endo_names,unique_variables(jvar))) , ismember(exo_names,jexo)),'-k','linewidth',2);
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hold on
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plot([1 irf_horizon],[0 0],'-r','linewidth',1);
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hold off
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xlim([1 irf_horizon]);
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remove_fractional_xticks
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if TeX
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title(['$' endo_names{unique_variables(jvar)} '$'],'Interpreter','latex');
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title(['$' endo_names_tex{ismember(endo_names,unique_variables(jvar))} '$'],'Interpreter','latex');
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else
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title(endo_names{unique_variables(jvar)},'Interpreter','none');
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title(unique_variables{jvar},'Interpreter','none');
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end
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set(gca,'FontSize',12);
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if (plt==nstar) || jvar==length(unique_variables)
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@ -101,17 +110,17 @@ for jexo = unique_shock_entries'
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lgd = legend([plt_data,plt_model],{'Data', 'Model'}, 'Location', 'southeast','NumColumns',2,'FontSize',14);
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lgd.Position = [0.37 0.01 lgd.Position(3) lgd.Position(4)];
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dyn_saveas(fig_irf,[graph_directory_name filesep fname '_matched_irf_' exo_names{jexo} int2str(fig)],nodisplay,graph_format);
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dyn_saveas(fig_irf,[graph_directory_name filesep fname '_matched_irf_' jexo{:} int2str(fig)],nodisplay,graph_format);
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if TeX && any(strcmp('eps',cellstr(graph_format)))
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fprintf(fid_TeX,'\\begin{figure}[H]\n');
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fprintf(fid_TeX,'\\centering \n');
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fprintf(fid_TeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_matched_irf_%s%s}\n',figures_textwidth*min(plt/nc,1),[graph_directory_name '/' fname],exo_names{jexo},int2str(fig));
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fprintf(fid_TeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_matched_irf_%s%s}\n',figures_textwidth*min(plt/nc,1),[graph_directory_name '/' fname],jexo{:},int2str(fig));
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if relative_irf
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fprintf(fid_TeX,'\\caption{Relative impulse response functions (orthogonalized shock to $%s$).}', exo_names_tex{jexo});
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fprintf(fid_TeX,'\\caption{Relative impulse response functions (orthogonalized shock to $%s$).}', jexo{:});
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else
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fprintf(fid_TeX,'\\caption{Impulse response functions (orthogonalized shock to $%s$).}', exo_names_tex{jexo});
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fprintf(fid_TeX,'\\caption{Impulse response functions (orthogonalized shock to $%s$).}', jexo{:});
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end
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fprintf(fid_TeX,'\\label{Fig:MatchedIRF:%s:%s}\n', exo_names{jexo},int2str(fig));
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fprintf(fid_TeX,'\\label{Fig:MatchedIRF:%s:%s}\n', jexo{:},int2str(fig));
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fprintf(fid_TeX,'\\end{figure}\n');
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fprintf(fid_TeX,' \n');
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end
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@ -1,5 +1,5 @@
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function [data_irfs, weight_mat, irf_index, max_irf_horizon] = matched_irfs_blocks(matched_irfs, matched_irfs_weight, varobs_id, obs_nbr, exo_nbr, endo_names)
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% [data_irfs, weight_mat, irf_index, max_irf_horizon] = matched_irfs_blocks(matched_irfs, matched_irfs_weight, varobs_id, obs_nbr, exo_nbr, endo_names)
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function [data_irfs, weight_mat, irf_index, max_irf_horizon] = matched_irfs_blocks(matched_irfs, matched_irfs_weight, varobs_id, obs_nbr, exo_nbr, endo_names, exo_names)
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% [data_irfs, weight_mat, irf_index, max_irf_horizon] = matched_irfs_blocks(matched_irfs, matched_irfs_weight, varobs_id, obs_nbr, exo_nbr, endo_names, exo_names)
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% -------------------------------------------------------------------------
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% Checks and transforms matched_irfs and matched_irfs_weight blocks
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% for further use in the estimation.
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@ -10,7 +10,8 @@ function [data_irfs, weight_mat, irf_index, max_irf_horizon] = matched_irfs_bloc
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% varobs_id: [vector] index for observable variables in endo_names
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% obs_nbr: [scalar] number of observable variables
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% exo_nbr: [scalar] number of exogenous variables
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% endo_names: [cell array] list of endogenous variables
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% endo_names: [cell array] names of endogenous variables
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% exo_names: [cell array] names of exogenous variables
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% -------------------------------------------------------------------------
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% OUTPUT
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% data_irfs: [matrix] IRFs for VAROBS as declared in matched_irfs block
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@ -39,29 +40,78 @@ function [data_irfs, weight_mat, irf_index, max_irf_horizon] = matched_irfs_bloc
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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 <https://www.gnu.org/licenses/>.
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% note matched_irfs block:
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% - each row in the cell contains a unique combination of var and varexo,
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% however the third column in each row is a nested cell with information
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% on periods, values and weights
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% - periods, values and weights can span several rows with different lengths of entries
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% - in some cases we need to duplicate values and/or weights
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% - at the end we want to have everything vectorized and the same length
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max_irf_horizon = max(cellfun(@(x) x(end), matched_irfs(:,1))); % get maximum IRF horizon
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% create full matrix where 1st dimension are IRF periods, 2nd dimension are variables as declared in VAROBS, 3rd dimension are shocks.
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data_irfs = NaN(max_irf_horizon,obs_nbr,exo_nbr);
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% overwrite NaN values if they are declared in matched_irfs block; remaining NaN values will be later ignored in the matching
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% get maximum IRF horizons
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max_irf_horizon = [];
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for jj = 1:size(matched_irfs,1)
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id_var = matched_irfs{jj,1}(1);
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max_irf_horizon = [max_irf_horizon; cell2mat(cellfun(@(c) c(:), matched_irfs{jj,3}(:,1), 'UniformOutput', false))];
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end
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max_irf_horizon = max(max_irf_horizon);
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% create full matrix where 1st dimension are IRF periods, 2nd dimension are variables as declared in VAROBS, 3rd dimension are shocks
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% idea: overwrite NaN values if they are declared in matched_irfs block; at the end the remaining NaN values will be removed
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data_irfs = NaN(max_irf_horizon,obs_nbr,exo_nbr);
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% create full empirical weighting matrix, identity matrix by default, i.e. all IRFs are equally important
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% idea: first specify full matrix and then reduce it using only entries that are declared in matched_irfs block
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weight_mat = speye(max_irf_horizon*obs_nbr*exo_nbr);
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for jj = 1:size(matched_irfs,1)
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id_var = find(ismember(endo_names,matched_irfs{jj,1}));
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id_varobs = find(varobs_id==id_var,1);
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id_shock = matched_irfs{jj,1}(2);
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id_irf_period = matched_irfs{jj,1}(3);
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irf_value = matched_irfs{jj,2};
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id_shock = find(ismember(exo_names,matched_irfs{jj,2}));
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if isempty(id_varobs)
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skipline;
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error('method_of_moments: You specified an IRF matching involving variable %s, but it is not declared as a varobs!',endo_names{id_var})
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end
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IRF_PERIODS = []; IRF_VALUES = []; IRF_WEIGHTS = [];
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for kk = 1:size(matched_irfs{jj,3},1)
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irf_periods = matched_irfs{jj,3}{kk,1};
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if length(unique(irf_periods)) < length(irf_periods) % row-specific check for unique periods
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error('method_of_moments: You specified an IRF matching involving variable %s and shock %s, but there were duplicate ''periods'' in the specification!',endo_names{id_var},exo_names{id_shock});
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end
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irf_values = matched_irfs{jj,3}{kk,2};
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if length(irf_values)==1
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irf_values = repmat(irf_values,length(irf_periods),1);
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end
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if length(irf_periods) ~= length(irf_values) % row-specific check for enough values
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error('method_of_moments: You specified an IRF matching involving variable %s and shock %s, but the length of ''periods'' does not match the length of ''values''!',endo_names{id_var},exo_names{id_shock});
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end
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irf_weights = matched_irfs{jj,3}{kk,3};
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if length(irf_weights)==1
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irf_weights = repmat(irf_weights,length(irf_periods),1);
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end
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if length(irf_periods) ~= length(irf_weights) % row-specific check for enough weights
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error('method_of_moments: You specified an IRF matching involving variable %s and shock %s, but the length of ''periods'' does not match the length of ''weights''!',endo_names{id_var},exo_names{id_shock});
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end
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IRF_PERIODS = [IRF_PERIODS; irf_periods(:)];
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IRF_VALUES = [IRF_VALUES; irf_values(:)];
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IRF_WEIGHTS = [IRF_WEIGHTS; irf_weights(:)];
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end
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if length(unique(irf_periods)) < length(irf_periods) % overall check for unique periods
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error('method_of_moments: You specified an IRF matching involving variable %s and shock %s, but there were duplicate ''periods'' in the specification!',endo_names{id_var},exo_names{id_shock});
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end
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for hh = 1:length(IRF_PERIODS)
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data_irfs(IRF_PERIODS(hh),id_varobs,id_shock) = IRF_VALUES(hh);
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if IRF_WEIGHTS(hh) ~= 1
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idweight_mat = sub2ind(size(data_irfs),IRF_PERIODS(hh),id_varobs,id_shock);
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weight_mat(idweight_mat,idweight_mat) = IRF_WEIGHTS(hh);
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end
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end
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data_irfs(id_irf_period,id_varobs,id_shock) = irf_value;
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end
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% create (full) empirical weighting matrix
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weight_mat = eye(max_irf_horizon*obs_nbr*exo_nbr); % identity matrix by default: all IRFs are equally important
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% fine-tune weighting matrix using matched_irfs_weights
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for jj = 1:size(matched_irfs_weight,1)
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id_var1 = matched_irfs_weight{jj,1}(1); id_varobs1 = find(varobs_id==id_var1,1); id_shock1 = matched_irfs_weight{jj,1}(2); id_irf_period1 = matched_irfs_weight{jj,1}(3);
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id_var2 = matched_irfs_weight{jj,2}(1); id_varobs2 = find(varobs_id==id_var2,1); id_shock2 = matched_irfs_weight{jj,2}(2); id_irf_period2 = matched_irfs_weight{jj,2}(3);
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weight_mat_value = matched_irfs_weight{jj,3};
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id_var1 = find(ismember(endo_names,matched_irfs_weight{jj,1}));
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id_var2 = find(ismember(endo_names,matched_irfs_weight{jj,4}));
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id_varobs1 = find(varobs_id==id_var1,1);
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id_varobs2 = find(varobs_id==id_var2,1);
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if isempty(id_varobs1)
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skipline;
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error('method_of_moments: You specified a weight for an IRF matching involving variable %s, but it is not a varobs!',endo_names{id_var1})
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@ -70,12 +120,32 @@ for jj = 1:size(matched_irfs_weight,1)
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skipline;
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error('method_of_moments: You specified a weight for an IRF matching involving variable %s, but it is not a varobs!',endo_names{id_var2})
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end
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idweight_mat1 = sub2ind(size(data_irfs),id_irf_period1,id_varobs1,id_shock1);
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idweight_mat2 = sub2ind(size(data_irfs),id_irf_period2,id_varobs2,id_shock2);
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weight_mat(idweight_mat1,idweight_mat2) = weight_mat_value;
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weight_mat(idweight_mat2,idweight_mat1) = weight_mat_value; % symmetry
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id_shock1 = find(ismember(exo_names,matched_irfs_weight{jj,3}));
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id_shock2 = find(ismember(exo_names,matched_irfs_weight{jj,6}));
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irf_periods1 = matched_irfs_weight{jj,2};
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irf_periods2 = matched_irfs_weight{jj,5};
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if length(irf_periods1) ~= length(irf_periods2)
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error('method_of_moments: You specified a ''matched_irfs_weights'' entry for an IRF matching involving %s/%s and %s/%s,\n but the horizons do not have the same length!',endo_names{id_var1},exo_names{id_shock1},endo_names{id_var2},exo_names{id_shock2});
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end
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if max([irf_periods1(:);irf_periods2(:)]) > max_irf_horizon
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error('method_of_moments: You specified a ''matched_irfs_weights'' entry for an IRF matching involving %s/%s and %s/%s,\n but the horizon is larger than the maximum one declared in the ''matched_irfs'' block!',endo_names{id_var1},exo_names{id_shock1},endo_names{id_var2},exo_names{id_shock2});
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end
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weight_mat_values = matched_irfs_weight{jj,7};
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if length(weight_mat_values)==1 && length(irf_periods1)>1
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weight_mat_values = repmat(weight_mat_values,length(irf_periods1),1);
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end
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if length(weight_mat_values) ~= length(irf_periods1)
|
||||
error('method_of_moments: You specified a ''matched_irfs_weights'' entry for an IRF matching involving %s/%s and %s/%s,\n but the horizons do not match the length of ''weights''!',endo_names{id_var1},exo_names{id_shock1},endo_names{id_var2},exo_names{id_shock2});
|
||||
end
|
||||
for hh = 1:length(irf_periods1)
|
||||
idweight_mat1 = sub2ind(size(data_irfs),irf_periods1(hh),id_varobs1,id_shock1);
|
||||
idweight_mat2 = sub2ind(size(data_irfs),irf_periods2(hh),id_varobs2,id_shock2);
|
||||
weight_mat(idweight_mat1,idweight_mat2) = weight_mat_values(hh);
|
||||
weight_mat(idweight_mat2,idweight_mat1) = weight_mat_values(hh); % symmetry
|
||||
end
|
||||
end
|
||||
% focus only on specified IRFs
|
||||
|
||||
% remove non-specified IRFs
|
||||
irf_index = find(~isnan(data_irfs));
|
||||
data_irfs = data_irfs(irf_index);
|
||||
weight_mat = weight_mat(irf_index,irf_index);
|
||||
|
|
@ -230,7 +230,7 @@ end
|
|||
% matched_irfs: checks and transformations
|
||||
% -------------------------------------------------------------------------
|
||||
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
|
||||
[oo_.mom.data_moments, oo_.mom.weighting_info.W, options_mom_.mom.irfIndex, options_mom_.irf] = mom.matched_irfs_blocks(M_.matched_irfs, M_.matched_irfs_weights, options_mom_.varobs_id, options_mom_.obs_nbr, M_.exo_nbr, M_.endo_names);
|
||||
[oo_.mom.data_moments, oo_.mom.weighting_info.W, options_mom_.mom.irfIndex, options_mom_.irf] = mom.matched_irfs_blocks(M_.matched_irfs, M_.matched_irfs_weights, options_mom_.varobs_id, options_mom_.obs_nbr, M_.exo_nbr, M_.endo_names, M_.exo_names);
|
||||
% compute inverse of weighting matrix
|
||||
try
|
||||
oo_.mom.weighting_info.Winv = inv(oo_.mom.weighting_info.W);
|
||||
|
|
@ -799,7 +799,7 @@ if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_meth
|
|||
oo_.mom.J_test = mom.Jtest(xparam1, objective_function, oo_.mom.Q, oo_.mom.model_moments, oo_.mom.m_data, oo_.mom.data_moments, oo_.mom.weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
|
||||
elseif strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
|
||||
if ~options_mom_.nograph
|
||||
mom.graph_comparison_irfs(M_.matched_irfs,oo_.mom.irf_model_varobs,options_mom_.varobs_id,options_mom_.irf,options_mom_.relative_irf,M_.endo_names,M_.exo_names,M_.exo_names_tex,M_.dname,M_.fname,options_mom_.graph_format,options_mom_.TeX,options_mom_.nodisplay,options_mom_.figures.textwidth)
|
||||
mom.graph_comparison_irfs(M_.matched_irfs,oo_.mom.irf_model_varobs,options_mom_.varobs_id,options_mom_.irf,options_mom_.relative_irf,M_.endo_names,M_.endo_names_tex,M_.exo_names,M_.exo_names_tex,M_.dname,M_.fname,options_mom_.graph_format,options_mom_.TeX,options_mom_.nodisplay,options_mom_.figures.textwidth)
|
||||
end
|
||||
end
|
||||
% display comparison of model moments/IRFs and data moments/IRFs
|
||||
|
|
|
|||
|
|
@ -910,39 +910,34 @@ mod_and_m_tests = [
|
|||
{ 'test' : [ 'estimation/method_of_moments/AFVRR/AFVRR_MFB_RRA.mod' ],
|
||||
'extra' : [ 'estimation/method_of_moments/AFVRR/AFVRR_common.inc',
|
||||
'estimation/method_of_moments/AFVRR/AFVRR_data.mat',
|
||||
'estimation/method_of_moments/AFVRR/AFVRR_steady_helper.m' ] },
|
||||
'estimation/method_of_moments/AFVRR/AFVRR_steady_helper.m' ] },
|
||||
{ 'test' : [ 'estimation/method_of_moments/CET/cet_imh.mod' ],
|
||||
'extra' : [ 'estimation/method_of_moments/CET/cet_data.mat',
|
||||
'estimation/method_of_moments/CET/cet_irf_matching_file.m',
|
||||
'estimation/method_of_moments/CET/cet_matched_irfs_no_interface_workaround.m',
|
||||
'estimation/method_of_moments/CET/cet_model.inc',
|
||||
'estimation/method_of_moments/CET/cet_original_mode.mat',
|
||||
'estimation/method_of_moments/CET/cet_steady_helper.m' ] },
|
||||
{ 'test' : [ 'estimation/method_of_moments/CET/cet_mle.mod' ],
|
||||
'extra' : [ 'estimation/method_of_moments/CET/cet_data.mat',
|
||||
'estimation/method_of_moments/CET/cet_irf_matching_file.m',
|
||||
'estimation/method_of_moments/CET/cet_matched_irfs_no_interface_workaround.m',
|
||||
'estimation/method_of_moments/CET/cet_model.inc',
|
||||
'estimation/method_of_moments/CET/cet_original_mode.mat',
|
||||
'estimation/method_of_moments/CET/cet_steady_helper.m' ] },
|
||||
{ 'test' : [ 'estimation/method_of_moments/CET/cet_rwmh.mod' ],
|
||||
'extra' : [ 'estimation/method_of_moments/CET/cet_data.mat',
|
||||
'estimation/method_of_moments/CET/cet_irf_matching_file.m',
|
||||
'estimation/method_of_moments/CET/cet_matched_irfs_no_interface_workaround.m',
|
||||
'estimation/method_of_moments/CET/cet_model.inc',
|
||||
'estimation/method_of_moments/CET/cet_original_mode.mat',
|
||||
'estimation/method_of_moments/CET/cet_steady_helper.m' ] },
|
||||
{ 'test' : [ 'estimation/method_of_moments/CET/cet_slice.mod' ],
|
||||
'extra' : [ 'estimation/method_of_moments/CET/cet_data.mat',
|
||||
'estimation/method_of_moments/CET/cet_irf_matching_file.m',
|
||||
'estimation/method_of_moments/CET/cet_matched_irfs_no_interface_workaround.m',
|
||||
'estimation/method_of_moments/CET/cet_model.inc',
|
||||
'estimation/method_of_moments/CET/cet_original_mode.mat',
|
||||
'estimation/method_of_moments/CET/cet_steady_helper.m' ] },
|
||||
{ 'test' : [ 'estimation/method_of_moments/CET/cet_tarb.mod' ],
|
||||
'extra' : [ 'estimation/method_of_moments/CET/cet_data.mat',
|
||||
'estimation/method_of_moments/CET/cet_irf_matching_file.m',
|
||||
'estimation/method_of_moments/CET/cet_matched_irfs_no_interface_workaround.m',
|
||||
'estimation/method_of_moments/CET/cet_model.inc',
|
||||
'estimation/method_of_moments/CET/cet_original_mode.mat',
|
||||
'estimation/method_of_moments/CET/cet_steady_helper.m' ] },
|
||||
|
|
|
|||
|
|
@ -25,13 +25,6 @@
|
|||
|
||||
options_.prior_interval= 0.95;
|
||||
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%
|
||||
%% NO INTERFACE YET %%
|
||||
%%%%%%%%%%%%%%%%%%%%%%
|
||||
[M_.matched_irfs, M_.matched_irfs_weights] = cet_matched_irfs_no_interface_workaround(M_.endo_names,M_.exo_names);
|
||||
|
||||
|
||||
method_of_moments(mom_method = irf_matching
|
||||
%, add_tiny_number_to_cholesky = 1e-14
|
||||
%, additional_optimizer_steps = [4]
|
||||
|
|
|
|||
|
|
@ -1,86 +0,0 @@
|
|||
function [matched_irfs, matched_irfs_weights] = cet_matched_irfs_no_interface_workaround(endo_names,exo_names)
|
||||
% [matched_irfs, matched_irfs_weights] = cet_matched_irfs_no_interface_workaround(endo_names,exo_names)
|
||||
% -------------------------------------------------------------------------
|
||||
% Based on replication codes for Christiano, Eichenbaum, Trabandt (2016, Econometrica) - Unemployment and the Business Cycle
|
||||
% This currently replaces the interface for the IRF Matching capabilities of the method_of_moments toolbox.
|
||||
% -------------------------------------------------------------------------
|
||||
|
||||
% Copyright © 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/>.
|
||||
|
||||
%% settings
|
||||
irf_horizon = 15; % horizon of impulse responses to match
|
||||
do_monetary_shock_only = 0; % if = 0 all shocks are used in estimation
|
||||
|
||||
%% load VAR impulse responses from Christiano, Trabandt and Walentin (2010)- Handbook of Monetary Economics Chapter
|
||||
% IRFFF: impulse responses with respect to monetary policy shock
|
||||
% IRFFz: impulse responses with respect to neutral tech shock
|
||||
% IRFFu: impulse responses with respect to invest tech shock
|
||||
% IRFFFSE, IRFzSE, IRFuSE contain the corresponding estimated standard errors
|
||||
% dimensions: rows are periods, columns are variables:
|
||||
% - column 1: gdp corresponds to GDPAGG + cumsum(muF)
|
||||
% - column 2: inflation corresponds to piAGG
|
||||
% - column 3: federal funds rate corresponds to RAGG
|
||||
% - column 4: capacity utilization corresponds to ukAGG
|
||||
% - column 5: total hours corresponds to lAGG
|
||||
% - column 6: real wage corresponds to wAGG + cumsum(muF)
|
||||
% - column 7: consumption corresponds to cAGG + cumsum(muF)
|
||||
% - column 7: investment corresponds to iAGG + cumsum(muF) + cumsum(mupsiF)
|
||||
% - column 8: rel. price investment corresponds to cumsum(pinvestAGG)
|
||||
% - column 10: unemployment rate corresponds to unempAGG
|
||||
% - column 11: vacancies corresponds to vTotAGG*u
|
||||
% - column 12: labor force
|
||||
% - column 13: separation rate
|
||||
% - column 14: job finding rate corresponds to fAGG*f
|
||||
load('cet_data','IRFz','IRFzSE','IRFFF','IRFFFSE','IRFu','IRFuSE');
|
||||
|
||||
%% map empirical irf data to a model variable
|
||||
% note that any further required transformations or manipulations to model variables (such as cumsum, adding muF and mupsiF)
|
||||
% as well as selection of which periods to match occurs in an extra function cet_irf_matching_file.m
|
||||
% if no such function is given then the mapping is exact and the whole horizon will be considered
|
||||
|
||||
% irfs with respect to monetary shock
|
||||
if do_monetary_shock_only
|
||||
SHOCKNAMES = {'epsR_eps'};
|
||||
else
|
||||
SHOCKNAMES = {'epsR_eps', 'muz_eps', 'mupsi_eps'};
|
||||
end
|
||||
VARNAMES = {'GDPAGG' 'piAGG' 'RAGG' 'ukAGG' 'lAGG' 'wAGG' 'cAGG' 'iAGG' 'pinvestAGG' 'unempAGG' 'vTotAGG' 'fAGG'};
|
||||
RESCALE = [1 400 400 1 1 1 1 1 1 100 1 100 ];
|
||||
idx=1;
|
||||
for jexo = 1:length(SHOCKNAMES)
|
||||
id_shock = strmatch(SHOCKNAMES{jexo},exo_names);
|
||||
if SHOCKNAMES{jexo}=="epsR_eps"
|
||||
IRF = -1*IRFFF(:,[1:11 14]); IRFSE = IRFFFSE(:,[1:11 14]);
|
||||
elseif SHOCKNAMES{jexo}=="muz_eps"
|
||||
IRF = IRFz(:,[1:11 14]); IRFSE = IRFzSE(:,[1:11 14]);
|
||||
elseif SHOCKNAMES{jexo}=="mupsi_eps"
|
||||
IRF = IRFu(:,[1:11 14]); IRFSE = IRFuSE(:,[1:11 14]);
|
||||
end
|
||||
for jvar = 1:length(VARNAMES)
|
||||
id_var = strmatch(VARNAMES{jvar},endo_names);
|
||||
for jirf=1:irf_horizon
|
||||
if IRF(jirf,jvar) ~= 0
|
||||
matched_irfs(idx,:) = {[id_var, id_shock, jirf], IRF(jirf,jvar)/RESCALE(jvar)};
|
||||
matched_irfs_weights(idx,:) = {[id_var, id_shock, jirf], [id_var, id_shock, jirf], 1/(IRFSE(jirf,jvar)/RESCALE(jvar))^2 };
|
||||
idx = idx+1;
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
end
|
||||
|
|
@ -24,13 +24,6 @@
|
|||
|
||||
options_.prior_interval= 0.95;
|
||||
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%
|
||||
%% NO INTERFACE YET %%
|
||||
%%%%%%%%%%%%%%%%%%%%%%
|
||||
[M_.matched_irfs, M_.matched_irfs_weights] = cet_matched_irfs_no_interface_workaround(M_.endo_names,M_.exo_names);
|
||||
|
||||
|
||||
method_of_moments(mom_method = irf_matching
|
||||
%, add_tiny_number_to_cholesky = 1e-14
|
||||
%, additional_optimizer_steps = [4]
|
||||
|
|
|
|||
|
|
@ -742,4 +742,103 @@ rhomupsi , , , , beta_pdf , 0.75, 0.100;
|
|||
end;
|
||||
@#endif
|
||||
|
||||
varobs GDPAGG piAGG RAGG ukAGG lAGG wAGG cAGG iAGG pinvestAGG unempAGG vTotAGG fAGG;
|
||||
varobs GDPAGG piAGG RAGG ukAGG lAGG wAGG cAGG iAGG pinvestAGG unempAGG vTotAGG fAGG;
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
%% IRF DATA TRANSFORMATIONS %%
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
load('cet_data','IRFz','IRFzSE','IRFFF','IRFFFSE','IRFu','IRFuSE');
|
||||
% IRFFF: impulse responses with respect to monetary policy shock
|
||||
% IRFFz: impulse responses with respect to neutral tech shock
|
||||
% IRFFu: impulse responses with respect to invest tech shock
|
||||
% IRFFFSE, IRFzSE, IRFuSE contain the corresponding estimated standard errors
|
||||
% dimensions: rows are periods, columns are variables:
|
||||
% - column 1: gdp corresponds to GDPAGG + cumsum(muF)
|
||||
% - column 2: inflation corresponds to piAGG
|
||||
% - column 3: federal funds rate corresponds to RAGG
|
||||
% - column 4: capacity utilization corresponds to ukAGG
|
||||
% - column 5: total hours corresponds to lAGG
|
||||
% - column 6: real wage corresponds to wAGG + cumsum(muF)
|
||||
% - column 7: consumption corresponds to cAGG + cumsum(muF)
|
||||
% - column 7: investment corresponds to iAGG + cumsum(muF) + cumsum(mupsiF)
|
||||
% - column 8: rel. price investment corresponds to cumsum(pinvestAGG)
|
||||
% - column 10: unemployment rate corresponds to unempAGG
|
||||
% - column 11: vacancies corresponds to vTotAGG*u
|
||||
% - column 12: labor force
|
||||
% - column 13: separation rate
|
||||
% - column 14: job finding rate corresponds to fAGG*f
|
||||
% transformations will be done in cet_irf_matching_file.m
|
||||
|
||||
% change sign of monetary policy shock irfs
|
||||
IRFFF = -1*IRFFF;
|
||||
% rescale some irfs
|
||||
IRFFF(:,[2 3] ) = IRFFF(:,[2 3] )./400; IRFFFSE(:,[2 3] ) = IRFFFSE(:,[2 3] )./400;
|
||||
IRFFF(:,[10 14]) = IRFFF(:,[10 14])./100; IRFFFSE(:,[10 14]) = IRFFFSE(:,[10 14])./100;
|
||||
IRFz( :,[2 3] ) = IRFz( :,[2 3] )./400; IRFzSE( :,[2 3] ) = IRFzSE( :,[2 3] )./400;
|
||||
IRFz( :,[10 14]) = IRFz( :,[10 14])./100; IRFzSE( :,[10 14]) = IRFzSE( :,[10 14])./100;
|
||||
IRFu( :,[2 3] ) = IRFu( :,[2 3] )./400; IRFuSE( :,[2 3] ) = IRFuSE( :,[2 3] )./400;
|
||||
IRFu( :,[10 14]) = IRFu( :,[10 14])./100; IRFuSE( :,[10 14]) = IRFuSE( :,[10 14])./100;
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
%% MATCHED_IRFS BLOCK %%
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% use anonymous functions to quickly access variables in matrices
|
||||
irfs_epsR_eps = @(j) IRFFF(2:15,j); % start in t=2 due to identification restrictions in SVAR
|
||||
irfs_muz_eps = @(j) IRFz(1:15,j);
|
||||
irfs_mupsi_eps = @(j) IRFu(1:15,j);
|
||||
weights_epsR_eps = @(j) 1./(IRFFFSE(2:15,j).^2); % start in t=2 due to identification restrictions in SVAR
|
||||
weights_muz_eps = @(j) 1./(IRFzSE(1:15,j).^2);
|
||||
weights_mupsi_eps = @(j) 1./(IRFuSE(1:15,j).^2);
|
||||
% for RAGG we also include t=1
|
||||
RAGG_epsR_eps = IRFFF(1:15,3);
|
||||
w_RAGG_epsR_eps = 1./(IRFFFSE(1:15,3).^2);
|
||||
|
||||
matched_irfs;
|
||||
var GDPAGG; varexo epsR_eps; periods 2:15; values (irfs_epsR_eps(1)); weights (weights_epsR_eps(1));
|
||||
var GDPAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(1)); weights (weights_muz_eps(1));
|
||||
var GDPAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(1)); weights (weights_mupsi_eps(1));
|
||||
|
||||
var piAGG; varexo epsR_eps; periods 2:15; values (irfs_epsR_eps(2)); weights (weights_epsR_eps(2));
|
||||
var piAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(2)); weights (weights_muz_eps(2));
|
||||
var piAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(2)); weights (weights_mupsi_eps(2));
|
||||
|
||||
var RAGG; varexo epsR_eps; periods 1:15; values (RAGG_epsR_eps); weights (w_RAGG_epsR_eps);
|
||||
var RAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(3)); weights (weights_muz_eps(3));
|
||||
var RAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(3)); weights (weights_mupsi_eps(3));
|
||||
|
||||
var ukAGG; varexo epsR_eps; periods 2:15; values (irfs_epsR_eps(4)); weights (weights_epsR_eps(4));
|
||||
var ukAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(4)); weights (weights_muz_eps(4));
|
||||
var ukAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(4)); weights (weights_mupsi_eps(4));
|
||||
|
||||
var lAGG; varexo epsR_eps; periods 2:15; values (irfs_epsR_eps(5)); weights (weights_epsR_eps(5));
|
||||
var lAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(5)); weights (weights_muz_eps(5));
|
||||
var lAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(5)); weights (weights_mupsi_eps(5));
|
||||
|
||||
var wAGG; varexo epsR_eps; periods 2:15; values (irfs_epsR_eps(6)); weights (weights_epsR_eps(6));
|
||||
var wAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(6)); weights (weights_muz_eps(6));
|
||||
var wAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(6)); weights (weights_mupsi_eps(6));
|
||||
|
||||
var cAGG; varexo epsR_eps; periods 2:15; values (irfs_epsR_eps(7)); weights (weights_epsR_eps(7));
|
||||
var cAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(7)); weights (weights_muz_eps(7));
|
||||
var cAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(7)); weights (weights_mupsi_eps(7));
|
||||
|
||||
var iAGG; varexo epsR_eps; periods 2:15; values (irfs_epsR_eps(8)); weights (weights_epsR_eps(8));
|
||||
var iAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(8)); weights (weights_muz_eps(8));
|
||||
var iAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(8)); weights (weights_mupsi_eps(8));
|
||||
|
||||
var pinvestAGG; varexo epsR_eps; periods 2:15; values (irfs_epsR_eps(9)); weights (weights_epsR_eps(9));
|
||||
var pinvestAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(9)); weights (weights_muz_eps(9));
|
||||
var pinvestAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(9)); weights (weights_mupsi_eps(9));
|
||||
|
||||
var unempAGG; varexo epsR_eps; periods 2:15; values (irfs_epsR_eps(10)); weights (weights_epsR_eps(10));
|
||||
var unempAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(10)); weights (weights_muz_eps(10));
|
||||
var unempAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(10)); weights (weights_mupsi_eps(10));
|
||||
|
||||
var vTotAGG; varexo epsR_eps; periods 2:15; values (irfs_epsR_eps(11)); weights (weights_epsR_eps(11));
|
||||
var vTotAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(11)); weights (weights_muz_eps(11));
|
||||
var vTotAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(11)); weights (weights_mupsi_eps(11));
|
||||
|
||||
var fAGG; varexo epsR_eps; periods 2:15; values (irfs_epsR_eps(14)); weights (weights_epsR_eps(14));
|
||||
var fAGG; varexo muz_eps; periods 1:15; values (irfs_muz_eps(14)); weights (weights_muz_eps(14));
|
||||
var fAGG; varexo mupsi_eps; periods 1:15; values (irfs_mupsi_eps(14)); weights (weights_mupsi_eps(14));
|
||||
end;
|
||||
|
|
@ -28,12 +28,6 @@
|
|||
options_.prior_interval= 0.95;
|
||||
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%
|
||||
%% NO INTERFACE YET %%
|
||||
%%%%%%%%%%%%%%%%%%%%%%
|
||||
[M_.matched_irfs, M_.matched_irfs_weights] = cet_matched_irfs_no_interface_workaround(M_.endo_names,M_.exo_names);
|
||||
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
%% RUN 1: FIND POSTERIOR MODE USING MODEFILE %%
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
|
|
|||
|
|
@ -28,12 +28,6 @@
|
|||
options_.prior_interval= 0.95;
|
||||
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%
|
||||
%% NO INTERFACE YET %%
|
||||
%%%%%%%%%%%%%%%%%%%%%%
|
||||
[M_.matched_irfs, M_.matched_irfs_weights] = cet_matched_irfs_no_interface_workaround(M_.endo_names,M_.exo_names);
|
||||
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
%% RUN 1: DEFAULT SLICE %%
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
|
|
|||
|
|
@ -26,12 +26,6 @@
|
|||
options_.prior_interval= 0.95;
|
||||
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%
|
||||
%% NO INTERFACE YET %%
|
||||
%%%%%%%%%%%%%%%%%%%%%%
|
||||
[M_.matched_irfs, M_.matched_irfs_weights] = cet_matched_irfs_no_interface_workaround(M_.endo_names,M_.exo_names);
|
||||
|
||||
|
||||
method_of_moments(mom_method = irf_matching
|
||||
%, add_tiny_number_to_cholesky = 1e-14
|
||||
, additional_optimizer_steps = [4]
|
||||
|
|
@ -168,7 +162,7 @@ method_of_moments(mom_method = irf_matching
|
|||
, irf_matching_file = cet_irf_matching_file
|
||||
, mh_conf_sig = 0.90
|
||||
, mh_replic=0
|
||||
, mode_check
|
||||
%, mode_check
|
||||
, mode_compute = 4
|
||||
, mode_file = 'cet_tarb_results/method_of_moments/cet_tarb_mh_mode'
|
||||
, plot_priors = 0
|
||||
|
|
|
|||
Loading…
Reference in New Issue