stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
dynare/matlab/DsgeLikelihood_hh.m

237 lines
7.9 KiB
Matlab
Raw Blame History

function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)
% function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)
% Evaluates the likelihood at each observation and the marginal density of a dsge model
% used in the optimization algorithm number 5
%
% INPUTS
% xparam1: vector of model parameters
% gend : scalar specifying the number of observations
% data : matrix of data
%
% OUTPUTS
% fval : value of the posterior kernel at xparam1
% llik : gives the density at each observation
% cost_flag : zero if the function returns a penalty, one otherwise
% ys : steady state of original endogenous variables
% trend_coeff :
% info : vector of informations about the penalty
%
% SPECIAL REQUIREMENTS
% Adapted from dsgelikelihood.m
%
% copyright marco.ratto@jrc.it [13-03-2007]
global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_ xparam1_test
fval = [];
ys = [];
trend_coeff = [];
xparam1_test = xparam1;
cost_flag = 1;
nobs = size(options_.varobs,1);
%------------------------------------------------------------------------------
% 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);
llik=fval;
cost_flag = 0;
info = 41;
return;
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);
llik=fval;
cost_flag = 0;
info = 42;
return;
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
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
for i=1:estim_params_.ncx
k1 =estim_params_.corrx(i,1);
k2 =estim_params_.corrx(i,2);
Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,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 = diag(eig(Q));
k = find(a < 0);
if k > 0
fval = bayestopt_.penalty+sum(-a(k));
llik=fval;
cost_flag = 0;
info = 43;
return
end
end
offset = offset+estim_params_.ncx;
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));
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
H(k2,k1) = H(k1,k2);
end
[CholH,testH] = chol(H);
if testH
a = diag(eig(H));
k = find(a < 0);
if k > 0
fval = bayestopt_.penalty+sum(-a(k));
llik=fval;
cost_flag = 0;
info = 44;
return
end
end
offset = offset+estim_params_.ncn;
end
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
% for i=1:estim_params_.np
% M_.params(estim_params_.param_vals(i,1)) = xparam1(i+offset);
%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
fval = bayestopt_.penalty+1;
llik=fval;
cost_flag = 0;
return
elseif info(1) == 3 | info(1) == 4 | info(1) == 20
fval = bayestopt_.penalty+info(2)^2;
llik=fval;
cost_flag = 0;
return
end
bayestopt_.mf = bayestopt_.mf1;
if ~options_.noconstant
if options_.loglinear == 1
constant = log(SteadyState(bayestopt_.mfys));
else
constant = SteadyState(bayestopt_.mfys);
end
else
constant = zeros(nobs,1);
end
if bayestopt_.with_trend == 1
trend_coeff = zeros(nobs,1);
t = options_.trend_coeffs;
for i=1:length(t)
if ~isempty(t{i})
trend_coeff(i) = evalin('base',t{i});
end
end
trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
else
trend = repmat(constant,1,gend);
end
start = options_.presample+1;
np = size(T,1);
mf = bayestopt_.mf;
%------------------------------------------------------------------------------
% 3. Initial condition of the Kalman filter
%------------------------------------------------------------------------------
if options_.lik_init == 1 % Kalman filter
Pstar = lyapunov_symm(T,R*Q*R');
Pinf = [];
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
Pstar = 10*eye(np);
Pinf = [];
elseif options_.lik_init == 3 % Diffuse Kalman filter
Pstar = zeros(np,np);
ivs = bayestopt_.restrict_var_list_stationary;
ivd = bayestopt_.restrict_var_list_nonstationary;
RR=T(:,bayestopt_.restrict_var_list_nonstationary);
i=find(abs(RR)>1.e-10);
R0=zeros(size(RR));
R0(i)=sign(RR(i));
Pinf=R0*R0';
T0 = T;
R1 = R;
for j=1:size(T,1),
for i=1:length(ivd),
T0(j,:) = T0(j,:)-RR(j,i).*T(ivd(i),:);
R1(j,:) = R1(j,:)-RR(j,i).*R(ivd(i),:);
end
end
Pstar = lyapunov_symm(T0,R1*Q*R1');
end
%------------------------------------------------------------------------------
% 4. Likelihood evaluation
%------------------------------------------------------------------------------
if any(any(H ~= 0)) % should be replaced by a flag
if options_.kalman_algo == 1
[LIK, lik] =DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,data,trend,start);
if isinf(LIK) & ~estim_params_.ncn %% The univariate approach considered here doesn't
%% apply when H has some off-diagonal elements.
[LIK, lik] =DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
elseif isinf(LIK) & estim_params_.ncn
[LIK, lik] =DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
end
elseif options_.kalman_algo == 3
if ~estim_params_.ncn %% The univariate approach considered here doesn't
%% apply when H has some off-diagonal elements.
[LIK, lik] =DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
else
[LIK, lik] =DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
end
end
else
if options_.kalman_algo == 1
%nv = size(bayestopt_.Z,1);
%LIK = kalman_filter(bayestopt_.Z,zeros(nv,nv),T,R,Q,data,zeros(size(T,1),1),Pstar,'u');
[LIK, lik] =DiffuseLikelihood1(T,R,Q,Pinf,Pstar,data,trend,start);
% LIK = diffuse_likelihood1(T,R,Q,Pinf,Pstar,data-trend,start);
%if abs(LIK1-LIK)>0.0000000001
% disp(['LIK1 and LIK are not equal! ' num2str(abs(LIK1-LIK))])
%end
if isinf(LIK)
[LIK, lik] =DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
end
elseif options_.kalman_algo == 3
[LIK, lik] =DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
end
end
if imag(LIK) ~= 0
likelihood = bayestopt_.penalty;
lik=ones(size(lik)).*bayestopt_.penalty;
else
likelihood = LIK;
end
% ------------------------------------------------------------------------------
% Adds prior if necessary
% ------------------------------------------------------------------------------
lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p1,bayestopt_.p2,bayestopt_.p3,bayestopt_.p4);
fval = (likelihood-lnprior);
llik=[-lnprior; .5*lik(start:end)];
Reference in New Issue View Git Blame Copy Permalink