diff --git a/matlab/compute_moments_varendo.m b/matlab/compute_moments_varendo.m
index a9ee769592843fded5e5e99ae4fc47b12ec28afe..8db1c516e1a4093a912bce381a4f1f9b09d7763a 100644
--- a/matlab/compute_moments_varendo.m
+++ b/matlab/compute_moments_varendo.m
@@ -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
diff --git a/matlab/conditional_variance_decomposition.m b/matlab/conditional_variance_decomposition.m
index bf8eef091676ab8c5dbacea9bfbba8909af8b949..b9893ad4e10d28a8d9309f77d1a0b14fb91b73ad 100644
--- a/matlab/conditional_variance_decomposition.m
+++ b/matlab/conditional_variance_decomposition.m
@@ -1,4 +1,4 @@
-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
diff --git a/matlab/conditional_variance_decomposition_mc_analysis.m b/matlab/conditional_variance_decomposition_mc_analysis.m
index 0968535f941accec960556f5b7fcb56b6812f93d..f96a67601e6c89c5ed40efe52bb5e17889204828 100644
--- a/matlab/conditional_variance_decomposition_mc_analysis.m
+++ b/matlab/conditional_variance_decomposition_mc_analysis.m
@@ -1,4 +1,5 @@
-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;']);
diff --git a/matlab/display_conditional_variance_decomposition.m b/matlab/display_conditional_variance_decomposition.m
index c4ad9b752741fff8001c16b9d904639c1b4a31f8..a75b95820865e19145f3e41fdc90bcf047e34e3f 100644
--- a/matlab/display_conditional_variance_decomposition.m
+++ b/matlab/display_conditional_variance_decomposition.m
@@ -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;
diff --git a/matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m b/matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m
index b35166dcb22f0ab6614f0e4408a2234289432c98..f7543882f33856376b0c9fcbe08655adf1b692bb 100644
--- a/matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m
+++ b/matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m
@@ -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<NumberOfConditionalDecompFiles-1
- 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);
else
clear('Conditional_decomposition_array');
end
diff --git a/matlab/posterior_moments.m b/matlab/posterior_moments.m
index c45cb3ed03883a48cf85db27cce636613708e352..c54ef93ebf9aacaba2c375eb5592f86fdd5b9975 100644
--- a/matlab/posterior_moments.m
+++ b/matlab/posterior_moments.m
@@ -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