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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
time-shift
Michel Juillard 2010-11-28 11:03:40 +01:00
parent a2f1ffa19c
commit 1fc8bbd6d3
6 changed files with 55 additions and 45 deletions
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2
matlab/compute_moments_varendo.m
View File

@ -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

28
matlab/conditional_variance_decomposition.m
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@ -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

37
matlab/conditional_variance_decomposition_mc_analysis.m
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@ -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;']);

8
matlab/display_conditional_variance_decomposition.m
View File

@ -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;

11
matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m
View File

@ -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

14
matlab/posterior_moments.m
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@ -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