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BVAR-DSGE with constant (not yet ready) 

git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1167 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
adjemian 2007-01-26 14:50:30 +00:00
parent 6c7fcaa567
commit 44093cbbb9
3 changed files with 87 additions and 79 deletions
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134
matlab/DsgeVarLikelihood.m
View File

@ -1,7 +1,6 @@
function [fval,cost_flag,ys,trend_coeff,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(xparam1,gend) function [fval,cost_flag,ys,trend_coeff,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(xparam1,gend)
% stephane.adjemian@ens.fr % stephane.adjemian@ens.fr
global bayestopt_ estim_params_ M_ options_ xparam1_test global bayestopt_ estim_params_ M_ options_
global xparam_
nvx = estim_params_.nvx; nvx = estim_params_.nvx;
nvn = estim_params_.nvn; nvn = estim_params_.nvn;
@ -29,71 +28,75 @@ cost_flag = 1;
nobs = size(options_.varobs,1); nobs = size(options_.varobs,1);
if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb) if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
k = find(xparam1 < bayestopt_.lb); k = find(xparam1 < bayestopt_.lb);
fval = bayestopt_.penalty*min(1e3,exp(sum(bayestopt_.lb(k)-xparam1(k)))); fval = bayestopt_.penalty*min(1e3,exp(sum(bayestopt_.lb(k)-xparam1(k))));
cost_flag = 0; cost_flag = 0;
return; return;
end end
if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub) if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
k = find(xparam1 > bayestopt_.ub); k = find(xparam1 > bayestopt_.ub);
fval = bayestopt_.penalty*min(1e3,exp(sum(xparam1(k)-bayestopt_.ub(k)))); fval = bayestopt_.penalty*min(1e3,exp(sum(xparam1(k)-bayestopt_.ub(k))));
cost_flag = 0; cost_flag = 0;
return; return;
end end
Q = M_.Sigma_e; Q = M_.Sigma_e;
for i=1:estim_params_.nvx for i=1:estim_params_.nvx
k = estim_params_.var_exo(i,1); k = estim_params_.var_exo(i,1);
Q(k,k) = xparam1(i)*xparam1(i); Q(k,k) = xparam1(i)*xparam1(i);
end end
offset = estim_params_.nvx; offset = estim_params_.nvx;
if estim_params_.nvn if estim_params_.nvn
H = zeros(nobs,nobs); disp('DsgeVarLikelihood :: Measurement errors are not implemented!')
for i=1:estim_params_.nvn return
k = estim_params_.var_endo(i,1); % $$$ H = zeros(nobs,nobs);
H(k,k) = xparam1(i+offset)*xparam1(i+offset); % $$$ for i=1:estim_params_.nvn
end % $$$ k = estim_params_.var_endo(i,1);
offset = offset+estim_params_.nvn; % $$$ H(k,k) = xparam1(i+offset)*xparam1(i+offset);
% $$$ end
% $$$ offset = offset+estim_params_.nvn;
end end
if estim_params_.ncx if estim_params_.ncx
for i=1:estim_params_.ncx for i=1:estim_params_.ncx
k1 =estim_params_.corrx(i,1); k1 =estim_params_.corrx(i,1);
k2 =estim_params_.corrx(i,2); k2 =estim_params_.corrx(i,2);
Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2)); Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
Q(k2,k1) = Q(k1,k2); Q(k2,k1) = Q(k1,k2);
end
[CholQ,testQ] = chol(Q);
if testQ%% The variance-covariance matrix of the structural innovations is not definite positive.
%% We have to compute the eigenvalues of this matrix in order to build the penalty.
a = eig(Q);
k = a<0;
if k > 0
fval = bayestopt_.penalty*min(1e3,exp(sum(-a(k))));
cost_flag = 0;
return
end end
end [CholQ,testQ] = chol(Q);
offset = offset+estim_params_.ncx; if testQ%% The variance-covariance matrix of the structural innovations is not definite positive.
%% We have to compute the eigenvalues of this matrix in order to build the penalty.
a = eig(Q);
k = a<0;
if k > 0
fval = bayestopt_.penalty*min(1e3,exp(sum(-a(k))));
cost_flag = 0;
return
end
end
offset = offset+estim_params_.ncx;
end end
if estim_params_.nvn & estim_params_.ncn if estim_params_.nvn & estim_params_.ncn
for i=1:estim_params_.ncn disp('DsgeVarLikelihood :: Measurement errors are not implemented!')
k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1)); return
k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2)); % $$$ for i=1:estim_params_.ncn
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2)); % $$$ k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
H(k2,k1) = H(k1,k2); % $$$ k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
end % $$$ H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
[CholH,testH] = chol(H); % $$$ H(k2,k1) = H(k1,k2);
if testH % $$$ end
a = eig(H); % $$$ [CholH,testH] = chol(H);
k = a<0; % $$$ if testH
if k > 0 % $$$ a = eig(H);
fval = bayestopt_.penalty*min(1e3,exp(sum(-a(k)))); % $$$ k = a<0;
cost_flag = 0; % $$$ if k > 0
return % $$$ fval = bayestopt_.penalty*min(1e3,exp(sum(-a(k))));
end % $$$ cost_flag = 0;
end % $$$ return
offset = offset+estim_params_.ncn; % $$$ end
% $$$ end
% $$$ offset = offset+estim_params_.ncn;
end end
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end); M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
M_.Sigma_e = Q; M_.Sigma_e = Q;
@ -107,27 +110,24 @@ dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
bayestopt_.restrict_columns,... bayestopt_.restrict_columns,...
bayestopt_.restrict_aux); bayestopt_.restrict_aux);
if info(1) == 1 | info(1) == 2 | info(1) == 5 if info(1) == 1 | info(1) == 2 | info(1) == 5
fval = bayestopt_.penalty; fval = bayestopt_.penalty;
cost_flag = 0; cost_flag = 0;
return return
elseif info(1) == 3 | info(1) == 4 | info(1) == 20 elseif info(1) == 3 | info(1) == 4 | info(1) == 20
fval = bayestopt_.penalty*min(1e3,exp(info(2))); fval = bayestopt_.penalty*min(1e3,exp(info(2)));
cost_flag = 0; cost_flag = 0;
return return
end end
if options_.loglinear == 1 if options_.loglinear == 1
constant = log(SteadyState(bayestopt_.mfys)); constant = log(SteadyState(bayestopt_.mfys));
else else
constant = SteadyState(bayestopt_.mfys); constant = SteadyState(bayestopt_.mfys);
end end
if bayestopt_.with_trend == 1 if bayestopt_.with_trend == 1
trend_coeff = zeros(nobs,1); disp('DsgeVarLikelihood :: Linear trend is not yet implemented!')
for i=1:nobs return
trend_coeff(i) = evalin('base',bayestopt_.trend_coeff{i}); % $$$ else
end % $$$ trend = constant*ones(1,gend);
trend = constant*ones(1,gend)+trend_coeff*(1:gend);
else
trend = constant*ones(1,gend);
end end
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------

28
matlab/dynare_estimation.m
View File

@ -140,7 +140,6 @@ if ~isempty(options_.unit_root_vars)
end end
i_ur(i) = i1; i_ur(i) = i1;
end end
[junk,bayestopt_.var_list_stationary] = ... [junk,bayestopt_.var_list_stationary] = ...
setdiff((1:M_.endo_nbr)',i_ur); setdiff((1:M_.endo_nbr)',i_ur);
[junk,bayestopt_.restrict_var_list_stationary] = ... [junk,bayestopt_.restrict_var_list_stationary] = ...
@ -194,22 +193,31 @@ if length(options_.mode_file) > 0 & options_.posterior_mode_estimation
eval(['load ' options_.mode_file ';']'); eval(['load ' options_.mode_file ';']');
end end
%% compute sample moments if needed (bvar-dsge)
if ~isempty(strmatch('dsge_prior_weight',M_.param_names)) %% Compute the steadyn state:
evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ... if options_.steadystate_flag% if the _steadystate.m file is provided.
'var_sample_moments(options_.first_obs,options_.first_obs+options_.nobs-1,options_.varlag,-1);']) [oo_.steady_state,tchek] = feval([M_.fname '_steadystate'],[],[]);
else% if the steady state file is not provided.
[dd,info] = resol(oo_.steady_state,0)
oo_.steady_state = dd.ys; clear('dd');
end end
%% Compute the steadyn state if the _steadystate.m file is provided
if options_.steadystate_flag
[oo_.steady_state,tchek] = feval([M_.fname '_steadystate'],[],[]);
end
initial_estimation_checks(xparam1,gend,data); initial_estimation_checks(xparam1,gend,data);
if options_.mode_compute == 0 & length(options_.mode_file) == 0 if options_.mode_compute == 0 & length(options_.mode_file) == 0
return; return;
end end
%% compute sample moments if needed (bvar-dsge)
if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
if all(abs(oo_.steady_state)<10e-9)
evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
'var_sample_moments(options_.first_obs,options_.first_obs+options_.nobs-1,options_.varlag,-1);'])
else% The steady state is non zero ==> a constant in the VAR is needed!
evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
'var_sample_moments(options_.first_obs,options_.first_obs+options_.nobs-1,options_.varlag,0);'])
end
end
%% Estimation of the posterior mode or likelihood mode %% Estimation of the posterior mode or likelihood mode
if options_.mode_compute > 0 & options_.posterior_mode_estimation if options_.mode_compute > 0 & options_.posterior_mode_estimation
@ -827,7 +835,7 @@ end
if ~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape ... if ~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape ...
> 0) & options_.load_mh_file)) | ~options_.smoother > 0) & options_.load_mh_file)) | ~options_.smoother
%% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variables %% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variable
options_.lik_algo = 2; options_.lik_algo = 2;
[atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(xparam1,gend,data); [atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(xparam1,gend,data);
for i=1:M_.endo_nbr for i=1:M_.endo_nbr

4
matlab/var_sample_moments.m
View File

@ -29,7 +29,7 @@ if trend == -1% No constant
X = zeros(NumberOfObservations,NumberOfVariables*qlag); X = zeros(NumberOfObservations,NumberOfVariables*qlag);
elseif trend == 0% Constant elseif trend == 0% Constant
X = zeros(NumberOfObservations,NumberOfVariables*qlag+1); X = zeros(NumberOfObservations,NumberOfVariables*qlag+1);
indx = NumberOfVariables*qlag+1; indx = NumberOfVariables*qlag+1:NumberOfVariables*qlag+NumberOfVariables;
elseif trend == 1;% Constant + Trend elseif trend == 1;% Constant + Trend
X = zeros(NumberOfObservations,NumberOfVariables*qlag+2); X = zeros(NumberOfObservations,NumberOfVariables*qlag+2);
indx = NumberOfVariables*qlag+1:NumberOfVariables*qlag+2; indx = NumberOfVariables*qlag+1:NumberOfVariables*qlag+2;
@ -45,7 +45,7 @@ for t=1:NumberOfObservations
X(t,(lag-1)*NumberOfVariables+1:lag*NumberOfVariables) = data(line-lag,:); X(t,(lag-1)*NumberOfVariables+1:lag*NumberOfVariables) = data(line-lag,:);
end end
if trend == 0 if trend == 0
X(t,indx) = 1; X(t,indx) = ones(1,NumberOfVariables);
elseif trend == 1 elseif trend == 1
X(t,indx) = [ 1 , t ]; X(t,indx) = [ 1 , t ];
end end