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Added estimation of bvar-dsge models with constant. 

git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1441 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
adjemian 2007-11-14 17:42:18 +00:00
parent 2a79c2cecc
commit c14493361b
3 changed files with 34 additions and 26 deletions
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48
matlab/DsgeVarLikelihood.m
View File

@ -14,8 +14,7 @@ function [fval,cost_flag,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(xparam1,gend)
% o iXX [double] inv(X'X).
%
% ALGORITHM
% None.
%
% None.
% SPECIAL REQUIREMENTS
% None.
%
@ -83,7 +82,7 @@ dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
% Is the DSGE prior proper?
if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/gend;
fval = bayestopt_.penalty*min(1e3,(NumberOfParameters+NumberOfObservedVariables)/gend-dsge_prior_weight);
info = 51
info = 51;
cost_flag = 0;
return;
end
@ -104,10 +103,15 @@ elseif info(1) == 3 | info(1) == 4 | info(1) == 20
cost_flag = 0;
return
end
if options_.loglinear == 1
constant = log(SteadyState(bayestopt_.mfys));
if ~options_.noconstant
if options_.loglinear
constant = transpose(log(SteadyState(bayestopt_.mfys)));
else
constant = transpose(SteadyState(bayestopt_.mfys));
end
NumberOfParameters = NumberOfParameters + 1;
else
constant = SteadyState(bayestopt_.mfys);
constant = zeros(1,NumberOfObservedVariables);
end
if bayestopt_.with_trend == 1
disp('DsgeVarLikelihood :: Linear trend is not yet implemented!')
@ -115,33 +119,41 @@ if bayestopt_.with_trend == 1
end
%------------------------------------------------------------------------------
% 3. theorretical moments (second order)
% 3. theoretical moments (second order)
%------------------------------------------------------------------------------
tmp0 = lyapunov_symm(T,R*Q*R');% I compute the variance-covariance matrix
% of the restricted state vector.
%bayestopt_.mf = bayestopt_.mf1;????????
mf = bayestopt_.mf1;
mf = bayestopt_.mf1; % of the restricted state vector.
% Get the non centered second order moments
TheoreticalAutoCovarianceOfTheObservedVariables = ...
zeros(NumberOfObservedVariables,NumberOfObservedVariables,NumberOfLags+1);
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) = tmp0(mf,mf);
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) = tmp0(mf,mf)+constant'*constant;
for lag = 1:NumberOfLags
tmp0 = T*tmp0;
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf);
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf) ...
+ constant'*constant;
end
% Build the theoretical "covariance" between Y and X
GYX = zeros(NumberOfObservedVariables,NumberOfParameters);
for i=1:NumberOfLags
GYX(:,(i-1)*NumberOfObservedVariables+1:i*NumberOfObservedVariables) = ...
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1);
end
if ~options_.noconstant
GYX(:,end) = constant';
end
% Build the theoretical "covariance" between X and X
GXX = kron(eye(NumberOfLags), ...
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1));
for i = 1:NumberOfLags-1
tmp1 = diag(ones(NumberOfLags-i,1),i);
tmp2 = diag(ones(NumberOfLags-i,1),-i);
GXX = GXX + kron(tmp1,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1));
GXX = GXX + kron(tmp2,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1)');
tmp1 = diag(ones(NumberOfLags-i,1),i);
tmp2 = diag(ones(NumberOfLags-i,1),-i);
GXX = GXX + kron(tmp1,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1));
GXX = GXX + kron(tmp2,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1)');
end
if ~options_.noconstant
% Add one row and one column to GXX
GXX = [GXX , ones(NumberOfLags*NumberOfObservedVariables,1) ; ones(1,NumberOfParameters)];
end
GYY = TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1);
@ -150,7 +162,7 @@ assignin('base','GYY',GYY);
assignin('base','GXX',GXX);
assignin('base','GYX',GYX);
if ~isinf(dsge_prior_weight)
if ~isinf(dsge_prior_weight)
tmp0 = dsge_prior_weight*gend*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
tmp1 = dsge_prior_weight*gend*GYX + mYX;
tmp2 = inv(dsge_prior_weight*gend*GXX+mXX);

4
matlab/dynare_estimation.m
View File

@ -25,9 +25,6 @@ end
if options_.prefilter == 1
options_.noconstant = 1;
end
if options_.bvar_dsge
options_.noconstant = 1;
end
if options_.filtered_vars ~= 0 & options_.filter_step_ahead == 0
options_.filter_step_ahead = 1;
@ -223,7 +220,6 @@ end
%% compute sample moments if needed (bvar-dsge)
if options_.bvar_dsge~isempty(strmatch('dsge_prior_weight',M_.param_names))
options_.noconstant = 1;
if options_.noconstant
evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
'var_sample_moments(options_.first_obs,options_.first_obs+options_.nobs-1,options_.varlag,-1);'])

8
matlab/var_sample_moments.m
View File

@ -82,7 +82,7 @@ if qlag > FirstObservation
end
NumberOfObservations = LastObservation-FirstObservation+1;% This is T.
NumberOfVariables = options_.varobs;% This is m.
NumberOfVariables = size(options_.varobs,1);% This is m.
if var_trend_order == -1% No constant no linear trend case.
X = zeros(NumberOfObservations,NumberOfVariables*qlag);
elseif var_trend_order == 0% Constant and no linear trend case.
@ -101,12 +101,12 @@ Y = data(FirstObservation:LastObservation,:);
for t=1:NumberOfObservations
line = t + FirstObservation-1;
for lag = 1:qlag
X(t,(lag-1)*NumberOfVariables+1:lag*NumberOfVariables) = data(line-lag,:);
X(t,(lag-1)*NumberOfVariables+1:lag*NumberOfVariables) = data(line-lag,:);
end
if var_trend_order == 0
X(t,indx) = ones(1,NumberOfVariables);
X(t,indx) = 1;
elseif var_trend_order == 1
X(t,indx) = [ 1 , t ];
X(t,indx) = [ 1 , t ];
end
end