Updated version following new revision of dsgelikelihood.m
git-svn-id: https://www.dynare.org/svn/dynare/trunk@3318 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
ratto
parent
e7adf28fbd
commit
16794e4ec9
|
|
@ -1,4 +1,4 @@
|
|||
function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood_hh(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)
|
||||
function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)
|
||||
% function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)
|
||||
% Evaluates the posterior kernel of a dsge model.
|
||||
%
|
||||
|
|
@ -38,45 +38,45 @@ function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood_hh(xparam1,g
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_
|
||||
fval = [];
|
||||
ys = [];
|
||||
trend_coeff = [];
|
||||
llik = NaN;
|
||||
cost_flag = 1;
|
||||
nobs = size(options_.varobs,1);
|
||||
%------------------------------------------------------------------------------
|
||||
% 1. Get the structural parameters & define penalties
|
||||
%------------------------------------------------------------------------------
|
||||
if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
|
||||
global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_
|
||||
fval = [];
|
||||
ys = [];
|
||||
trend_coeff = [];
|
||||
cost_flag = 1;
|
||||
nobs = size(options_.varobs,1);
|
||||
llik=NaN;
|
||||
%------------------------------------------------------------------------------
|
||||
% 1. Get the structural parameters & define penalties
|
||||
%------------------------------------------------------------------------------
|
||||
if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
|
||||
k = find(xparam1 < bayestopt_.lb);
|
||||
fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
|
||||
cost_flag = 0;
|
||||
info = 41;
|
||||
return;
|
||||
end
|
||||
if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
|
||||
end
|
||||
if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
|
||||
k = find(xparam1 > bayestopt_.ub);
|
||||
fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
|
||||
cost_flag = 0;
|
||||
info = 42;
|
||||
return;
|
||||
end
|
||||
Q = M_.Sigma_e;
|
||||
H = M_.H;
|
||||
for i=1:estim_params_.nvx
|
||||
end
|
||||
Q = M_.Sigma_e;
|
||||
H = M_.H;
|
||||
for i=1:estim_params_.nvx
|
||||
k =estim_params_.var_exo(i,1);
|
||||
Q(k,k) = xparam1(i)*xparam1(i);
|
||||
end
|
||||
offset = estim_params_.nvx;
|
||||
if estim_params_.nvn
|
||||
end
|
||||
offset = estim_params_.nvx;
|
||||
if estim_params_.nvn
|
||||
for i=1:estim_params_.nvn
|
||||
k = estim_params_.var_endo(i,1);
|
||||
H(k,k) = xparam1(i+offset)*xparam1(i+offset);
|
||||
end
|
||||
offset = offset+estim_params_.nvn;
|
||||
end
|
||||
if estim_params_.ncx
|
||||
end
|
||||
if estim_params_.ncx
|
||||
for i=1:estim_params_.ncx
|
||||
k1 =estim_params_.corrx(i,1);
|
||||
k2 =estim_params_.corrx(i,2);
|
||||
|
|
@ -96,8 +96,8 @@ if estim_params_.ncx
|
|||
end
|
||||
end
|
||||
offset = offset+estim_params_.ncx;
|
||||
end
|
||||
if estim_params_.ncn
|
||||
end
|
||||
if estim_params_.ncn
|
||||
for i=1:estim_params_.ncn
|
||||
k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
|
||||
k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
|
||||
|
|
@ -116,38 +116,38 @@ if estim_params_.ncn
|
|||
end
|
||||
end
|
||||
offset = offset+estim_params_.ncn;
|
||||
end
|
||||
if estim_params_.np > 0
|
||||
end
|
||||
if estim_params_.np > 0
|
||||
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
|
||||
end
|
||||
M_.Sigma_e = Q;
|
||||
M_.H = H;
|
||||
%------------------------------------------------------------------------------
|
||||
% 2. call model setup & reduction program
|
||||
%------------------------------------------------------------------------------
|
||||
[T,R,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
|
||||
end
|
||||
M_.Sigma_e = Q;
|
||||
M_.H = H;
|
||||
%------------------------------------------------------------------------------
|
||||
% 2. call model setup & reduction program
|
||||
%------------------------------------------------------------------------------
|
||||
[T,R,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
|
||||
bayestopt_.restrict_columns,...
|
||||
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+1;
|
||||
cost_flag = 0;
|
||||
return
|
||||
elseif info(1) == 3 | info(1) == 4 | info(1) == 20
|
||||
fval = bayestopt_.penalty+info(2);%^2; % penalty power raised in DR1.m and resol already. GP July'08
|
||||
elseif info(1) == 3 || info(1) == 4 || info(1)==6 ||info(1) == 19 || info(1) == 20 || info(1) == 21
|
||||
fval = bayestopt_.penalty+info(2);
|
||||
cost_flag = 0;
|
||||
return
|
||||
end
|
||||
bayestopt_.mf = bayestopt_.mf1;
|
||||
if ~options_.noconstant
|
||||
if options_.loglinear == 1
|
||||
end
|
||||
bayestopt_.mf = bayestopt_.mf1;
|
||||
if options_.noconstant
|
||||
constant = zeros(nobs,1);
|
||||
else
|
||||
if options_.loglinear
|
||||
constant = log(SteadyState(bayestopt_.mfys));
|
||||
else
|
||||
constant = SteadyState(bayestopt_.mfys);
|
||||
end
|
||||
else
|
||||
constant = zeros(nobs,1);
|
||||
end
|
||||
if bayestopt_.with_trend == 1
|
||||
end
|
||||
if bayestopt_.with_trend
|
||||
trend_coeff = zeros(nobs,1);
|
||||
t = options_.trend_coeffs;
|
||||
for i=1:length(t)
|
||||
|
|
@ -156,30 +156,30 @@ if bayestopt_.with_trend == 1
|
|||
end
|
||||
end
|
||||
trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
|
||||
else
|
||||
else
|
||||
trend = repmat(constant,1,gend);
|
||||
end
|
||||
start = options_.presample+1;
|
||||
np = size(T,1);
|
||||
mf = bayestopt_.mf;
|
||||
no_missing_data_flag = (number_of_observations==gend*nobs);
|
||||
%------------------------------------------------------------------------------
|
||||
% 3. Initial condition of the Kalman filter
|
||||
%------------------------------------------------------------------------------
|
||||
kalman_algo = options_.kalman_algo;
|
||||
if options_.lik_init == 1 % Kalman filter
|
||||
end
|
||||
start = options_.presample+1;
|
||||
np = size(T,1);
|
||||
mf = bayestopt_.mf;
|
||||
no_missing_data_flag = (number_of_observations==gend*nobs);
|
||||
%------------------------------------------------------------------------------
|
||||
% 3. Initial condition of the Kalman filter
|
||||
%------------------------------------------------------------------------------
|
||||
kalman_algo = options_.kalman_algo;
|
||||
if options_.lik_init == 1 % Kalman filter
|
||||
if kalman_algo ~= 2
|
||||
kalman_algo = 1;
|
||||
end
|
||||
Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium,options_.lyapunov_complex_threshold);
|
||||
Pinf = [];
|
||||
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
|
||||
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
|
||||
if kalman_algo ~= 2
|
||||
kalman_algo = 1;
|
||||
end
|
||||
Pstar = options_.Harvey_scale_factor*eye(np);
|
||||
Pinf = [];
|
||||
elseif options_.lik_init == 3 % Diffuse Kalman filter
|
||||
elseif options_.lik_init == 3 % Diffuse Kalman filter
|
||||
if kalman_algo ~= 4
|
||||
kalman_algo = 3;
|
||||
end
|
||||
|
|
@ -240,27 +240,17 @@ elseif options_.lik_init == 3 % Diffuse Kalman filter
|
|||
dd(k1) = zeros(length(k1),1);
|
||||
Pinf(1:nk,1:nk) = diag(dd);
|
||||
end
|
||||
end
|
||||
if kalman_algo == 2
|
||||
no_correlation_flag = 1;
|
||||
if length(H)==1
|
||||
H = zeros(nobs,1);
|
||||
else
|
||||
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
||||
H = diag(H);
|
||||
else
|
||||
no_correlation_flag = 1;
|
||||
end
|
||||
if kalman_algo == 2
|
||||
end
|
||||
end
|
||||
kalman_tol = options_.kalman_tol;
|
||||
riccati_tol = options_.riccati_tol;
|
||||
mf = bayestopt_.mf1;
|
||||
Y = data-trend;
|
||||
%------------------------------------------------------------------------------
|
||||
% 4. Likelihood evaluation
|
||||
%------------------------------------------------------------------------------
|
||||
if (kalman_algo==1)% Multivariate Kalman Filter
|
||||
kalman_tol = options_.kalman_tol;
|
||||
riccati_tol = options_.riccati_tol;
|
||||
mf = bayestopt_.mf1;
|
||||
Y = data-trend;
|
||||
%------------------------------------------------------------------------------
|
||||
% 4. Likelihood evaluation
|
||||
%------------------------------------------------------------------------------
|
||||
if (kalman_algo==1)% Multivariate Kalman Filter
|
||||
if no_missing_data_flag
|
||||
[LIK, lik] = kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol);
|
||||
else
|
||||
|
|
@ -271,50 +261,69 @@ if (kalman_algo==1)% Multivariate Kalman Filter
|
|||
if isinf(LIK)
|
||||
kalman_algo = 2;
|
||||
end
|
||||
end
|
||||
if (kalman_algo==2)% Univariate Kalman Filter
|
||||
end
|
||||
if (kalman_algo==2)% Univariate Kalman Filter
|
||||
no_correlation_flag = 1;
|
||||
if length(H)==1 & H == 0
|
||||
H = zeros(nobs,1);
|
||||
else
|
||||
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
||||
H = diag(H);
|
||||
else
|
||||
no_correlation_flag = 0;
|
||||
end
|
||||
end
|
||||
if no_correlation_flag
|
||||
[LIK, lik] = univariate_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
||||
else
|
||||
[LIK, lik] = univariate_kalman_filter_corr(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
|
||||
end
|
||||
end
|
||||
if (kalman_algo==3)% Multivariate Diffuse Kalman Filter
|
||||
end
|
||||
if (kalman_algo==3)% Multivariate Diffuse Kalman Filter
|
||||
if no_missing_data_flag
|
||||
data1 = data - trend;
|
||||
if any(any(H ~= 0))
|
||||
[LIK, lik] = DiffuseLikelihoodH1_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,start);
|
||||
[LIK, lik] = diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol, ...
|
||||
riccati_tol);
|
||||
else
|
||||
[LIK, lik] = DiffuseLikelihood1_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
|
||||
[LIK, lik] = missing_observations_diffuse_kalman_filter(ST,R1,Q,H,Pinf, ...
|
||||
Pstar,Y,start,Z,kalman_tol,riccati_tol,...
|
||||
data_index,number_of_observations,...
|
||||
no_more_missing_observations);
|
||||
end
|
||||
if isinf(LIK)
|
||||
kalman_algo = 4;
|
||||
end
|
||||
else
|
||||
error(['The diffuse filter is not yet implemented for models with missing observations'])
|
||||
end
|
||||
end
|
||||
if (kalman_algo==4)% Univariate Diffuse Kalman Filter
|
||||
data1 = data - trend;
|
||||
if any(any(H ~= 0))
|
||||
if ~estim_params_.ncn
|
||||
[LIK, lik] = DiffuseLikelihoodH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
|
||||
if (kalman_algo==4)% Univariate Diffuse Kalman Filter
|
||||
no_correlation_flag = 1;
|
||||
if length(H)==1 & H == 0
|
||||
H = zeros(nobs,1);
|
||||
else
|
||||
[LIK, lik] = DiffuseLikelihoodH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
|
||||
end
|
||||
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
||||
H = diag(H);
|
||||
else
|
||||
[LIK, lik] = DiffuseLikelihood3_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
|
||||
no_correlation_flag = 0;
|
||||
end
|
||||
end
|
||||
if imag(LIK) ~= 0
|
||||
end
|
||||
if no_correlation_flag
|
||||
[LIK, lik] = univariate_diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y, ...
|
||||
start,Z,kalman_tol,riccati_tol,data_index,...
|
||||
number_of_observations,no_more_missing_observations);
|
||||
else
|
||||
[LIK, lik] = univariate_diffuse_kalman_filter_corr(ST,R1,Q,H,Pinf,Pstar, ...
|
||||
Y,start,Z,kalman_tol,riccati_tol,...
|
||||
data_index,number_of_observations,...
|
||||
no_more_missing_observations);
|
||||
end
|
||||
end
|
||||
if imag(LIK) ~= 0
|
||||
likelihood = bayestopt_.penalty;
|
||||
else
|
||||
else
|
||||
likelihood = LIK;
|
||||
end
|
||||
% ------------------------------------------------------------------------------
|
||||
% Adds prior if necessary
|
||||
% ------------------------------------------------------------------------------
|
||||
lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
||||
fval = (likelihood-lnprior);
|
||||
options_.kalman_algo = kalman_algo;
|
||||
llik=[-lnprior; lik(start:end)];
|
||||
end
|
||||
% ------------------------------------------------------------------------------
|
||||
% Adds prior if necessary
|
||||
% ------------------------------------------------------------------------------
|
||||
lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
||||
fval = (likelihood-lnprior);
|
||||
options_.kalman_algo = kalman_algo;
|
||||
llik=[-lnprior; lik(start:end)];
|
||||
|
|
|
|||
Loading…
Reference in New Issue