Commit 1fc8bbd6 by Michel Juillard

### bugs correction in computation of posterior moments for

```-conditional variance decomposition
-hpdsup
-moments with no variance in their posterior distribution
modification of computation of conditional variance decomposition```
parent a2f1ffa1
 ... ... @@ -113,7 +113,7 @@ if M_.exo_nbr > 1 if posterior for i=1:NumberOfEndogenousVariables for j=1:NumberOfExogenousVariables oo_ = posterior_analysis('conditional decomposition',var_list_(i,:),M_.exo_names(j,:),Steps,options_,M_,oo_); oo_ = posterior_analysis('conditional decomposition',i,M_.exo_names(j,:),Steps,options_,M_,oo_); end end else ... ...
 function PackedConditionalVarianceDecomposition = conditional_variance_decomposition(StateSpaceModel, Steps, SubsetOfVariables,sigma_e_is_diagonal) function ConditionalVarianceDecomposition = conditional_variance_decomposition(StateSpaceModel, Steps, SubsetOfVariables,sigma_e_is_diagonal) % This function computes the conditional variance decomposition of a given state space model % for a subset of endogenous variables. % ... ... @@ -8,9 +8,10 @@ function PackedConditionalVarianceDecomposition = conditional_variance_decomposi % SubsetOfVariables [integer] 1*q vector of indices. % % OUTPUTS % PackedConditionalVarianceDecomposition [double] n(n+1)/2*p matrix, where p is the number of state innovations and % n is equal to length(SubsetOfVariables). % % ConditionalVarianceDecomposition [double] [n h p] array, where % n is equal to length(SubsetOfVariables) % h is the number of Steps % p is the number of state innovations and % SPECIAL REQUIREMENTS % % [1] In this version, absence of measurement errors is assumed... ... ... @@ -37,11 +38,10 @@ number_of_state_innovations = ... transition_matrix = StateSpaceModel.transition_matrix; number_of_state_equations = ... StateSpaceModel.number_of_state_equations; order_var = StateSpaceModel.order_var; nSteps = length(Steps); ConditionalVariance = zeros(number_of_state_equations,number_of_state_equations); ConditionalVariance = repmat(ConditionalVariance,[1 1 nSteps ... number_of_state_innovations]); ConditionalVariance = zeros(number_of_state_equations,nSteps,number_of_state_innovations); if StateSpaceModel.sigma_e_is_diagonal B = StateSpaceModel.impulse_matrix.* ... ... ... @@ -58,17 +58,23 @@ for i=1:number_of_state_innovations for h = 1:max(Steps) V = transition_matrix*V*transition_matrix'+BB; if h == Steps(m) ConditionalVariance(:,:,m,i) = V; ConditionalVariance(order_var,m,i) = diag(V); m = m+1; end end end ConditionalVariance = ConditionalVariance(SubsetOfVariables,SubsetOfVariables,:,:); ConditionalVariance = ConditionalVariance(SubsetOfVariables,:,:); NumberOfVariables = length(SubsetOfVariables); PackedConditionalVarianceDecomposition = zeros(NumberOfVariables*(NumberOfVariables+1)/2,length(Steps),StateSpaceModel.number_of_state_innovations); SumOfVariances = zeros(NumberOfVariables,nSteps); for h = 1:length(Steps) SumOfVariances(:,h) = sum(ConditionalVariance(:,h,:),3); end ConditionalVarianceDecomposition = zeros(NumberOfVariables,length(Steps),number_of_state_innovations); for i=1:number_of_state_innovations for h = 1:length(Steps) PackedConditionalVarianceDecomposition(:,h,i) = dyn_vech(ConditionalVariance(:,:,h,i)); ConditionalVarianceDecomposition(:,h,i) = squeeze(ConditionalVariance(:,h,i))./SumOfVariances(:,h); end end \ No newline at end of file
 function oo_ = conditional_variance_decomposition_mc_analysis(NumberOfSimulations, type, dname, fname, Steps, exonames, exo, vartan, var, mh_conf_sig, oo_) function oo_ = ... conditional_variance_decomposition_mc_analysis(NumberOfSimulations, type, dname, fname, Steps, exonames, exo, var_list, endogenous_variable_index, mh_conf_sig, oo_) % This function analyses the (posterior or prior) distribution of the % endogenous conditional variance decomposition. ... ... @@ -27,19 +28,19 @@ else PATH = [dname '/prior/moments/']; end indx = check_name(vartan,var); if isempty(indx) disp([ type '_analysis:: ' var ' is not a stationary endogenous variable!']) return end endogenous_variable_index = sum(1:indx); % \$\$\$ indx = check_name(vartan,var); % \$\$\$ if isempty(indx) % \$\$\$ disp([ type '_analysis:: ' var ' is not a stationary endogenous variable!']) % \$\$\$ return % \$\$\$ end % \$\$\$ endogenous_variable_index = sum(1:indx); exogenous_variable_index = check_name(exonames,exo); if isempty(exogenous_variable_index) disp([ type '_analysis:: ' exo ' is not a declared exogenous variable!']) return end name = [ var '.' exo ]; name = [ var_list(endogenous_variable_index,:) '.' exo ]; if isfield(oo_, [ TYPE 'TheoreticalMoments' ]) eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;']) if isfield(temporary_structure,'dsge') ... ... @@ -75,17 +76,15 @@ p_density = NaN(2^9,2,length(Steps)); p_hpdinf = NaN(1,length(Steps)); p_hpdsup = NaN(1,length(Steps)); for i=1:length(Steps) if ~isconst(tmp(:,i)) [pp_mean, pp_median, pp_var, hpd_interval, pp_deciles, pp_density] = ... posterior_moments(tmp(:,i),1,mh_conf_sig); p_mean(2,i) = pp_mean; p_median(i) = pp_median; p_variance(i) = pp_var; p_deciles(:,i) = pp_deciles; p_hpdinf(i) = hpd_interval(1); p_hpdinf(i) = hpd_interval(2); p_density(:,:,i) = pp_density; end [pp_mean, pp_median, pp_var, hpd_interval, pp_deciles, pp_density] = ... posterior_moments(tmp(:,i),1,mh_conf_sig); p_mean(2,i) = pp_mean; p_median(i) = pp_median; p_variance(i) = pp_var; p_deciles(:,i) = pp_deciles; p_hpdinf(i) = hpd_interval(1); p_hpdsup(i) = hpd_interval(2); p_density(:,:,i) = pp_density; end eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.mean.' name ' = p_mean;']); eval(['oo_.' TYPE 'TheoreticalMoments.dsge.ConditionalVarianceDecomposition.median.' name ' = p_median;']); ... ...
 ... ... @@ -44,8 +44,9 @@ ic = dr.nstatic+(1:dr.npred)'; [StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,iv,ic,[],exo_nbr); StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e; StateSpaceModel.order_var = dr.order_var; conditional_decomposition_array = conditional_variance_decomposition(StateSpaceModel,Steps,dr.inv_order_var(SubsetOfVariables )); conditional_decomposition_array = conditional_variance_decomposition(StateSpaceModel,Steps,SubsetOfVariables ); if options_.noprint == 0 disp(' ') ... ... @@ -58,10 +59,9 @@ for i=1:length(Steps) disp(['Period ' int2str(Steps(i)) ':']) for j=1:exo_nbr vardec_i(:,j) = dyn_diag_vech(conditional_decomposition_array(:, ... i,j)); vardec_i(:,j) = 100*conditional_decomposition_array(:, ... i,j); end vardec_i = 100*vardec_i./repmat(sum(vardec_i,2),1,exo_nbr); if options_.noprint == 0 headers = M_.exo_names; headers(M_.exo_names_orig_ord,:) = headers; ... ...
 ... ... @@ -67,14 +67,14 @@ nar = options_.ar; options_.ar = 0; NumberOfDrawsFiles = rows(DrawsFiles); NumberOfSavedElementsPerSimulation = nvar*(nvar+1)/2*M_.exo_nbr*length(Steps); NumberOfSavedElementsPerSimulation = nvar*M_.exo_nbr*length(Steps); MaXNumberOfConditionalDecompLines = ceil(options_.MaxNumberOfBytes/NumberOfSavedElementsPerSimulation/8); if SampleSize<=MaXNumberOfConditionalDecompLines Conditional_decomposition_array = zeros(nvar*(nvar+1)/2,length(Steps),M_.exo_nbr,SampleSize); Conditional_decomposition_array = zeros(nvar,length(Steps),M_.exo_nbr,SampleSize); NumberOfConditionalDecompFiles = 1; else Conditional_decomposition_array = zeros(nvar*(nvar+1)/2,length(Steps),M_.exo_nbr,MaXNumberOfConditionalDecompLines); Conditional_decomposition_array = zeros(nvar,length(Steps),M_.exo_nbr,MaXNumberOfConditionalDecompLines); NumberOfLinesInTheLastConditionalDecompFile = mod(SampleSize,MaXNumberOfConditionalDecompLines); NumberOfConditionalDecompFiles = ceil(SampleSize/MaXNumberOfConditionalDecompLines); end ... ... @@ -118,6 +118,7 @@ for file = 1:NumberOfDrawsFiles StateSpaceModel.number_of_state_equations = M_.endo_nbr+rows(aux); StateSpaceModel.number_of_state_innovations = M_.exo_nbr; StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal; StateSpaceModel.order_var = dr.order_var; first_call = 0; clear('endo_nbr','nstatic','npred','k'); end ... ... @@ -136,10 +137,10 @@ for file = 1:NumberOfDrawsFiles 'Conditional_decomposition_array'); end if (ConditionalDecompFileNumber==NumberOfConditionalDecompFiles-1)% Prepare last round. Conditional_decomposition_array = zeros(nvar*(nvar+1)/2, length(Steps),M_.exo_nbr,NumberOfLinesInTheLastConditionalDecompFile) ; Conditional_decomposition_array = zeros(nvar, length(Steps),M_.exo_nbr,NumberOfLinesInTheLastConditionalDecompFile) ; NumberOfConditionalDecompLines = NumberOfLinesInTheLastConditionalDecompFile; elseif ConditionalDecompFileNumber
 ... ... @@ -70,9 +70,13 @@ post_deciles = xx([round(0.1*number_of_draws) ... density = []; if info number_of_grid_points = 2^9; % 2^9 = 512 !... Must be a power of two. bandwidth = 0; % Rule of thumb optimal bandwidth parameter. kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourrier Transform approximaton. optimal_bandwidth = mh_optimal_bandwidth(xx,number_of_draws,bandwidth,kernel_function); [density(:,1),density(:,2)] = kernel_density_estimate(xx,number_of_grid_points,... number_of_draws,optimal_bandwidth,kernel_function); if post_var > 0 bandwidth = 0; % Rule of thumb optimal bandwidth parameter. kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourrier Transform approximaton. optimal_bandwidth = mh_optimal_bandwidth(xx,number_of_draws,bandwidth,kernel_function); [density(:,1),density(:,2)] = kernel_density_estimate(xx,number_of_grid_points,... number_of_draws,optimal_bandwidth,kernel_function); else density = NaN(number_of_grid_points,2); end end \ No newline at end of file
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