function [LIK, lik] = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,Y,trend,start)
% function [LIK, lik] = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,Y,trend,start)
% Computes the diffuse likelihood (H=measurement error) in the case of a non-singular var-cov matrix
%
% INPUTS
% T: mm*mm matrix
% R: mm*rr matrix
% Q: rr*rr matrix
% H: pp*pp matrix
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
% Pstar: mm*mm variance-covariance matrix with stationary variables
% Y: pp*1 vector
% trend
% start: likelihood evaluation at 'start'
%
% OUTPUTS
% LIK: likelihood
% lik: density vector in each period
%
% SPECIAL REQUIREMENTS
% See "Filtering and Smoothing of State Vector for Diffuse State Space
% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
% Analysis, vol. 24(1), pp. 85-98).
% Copyright (C) 2005-2008 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see .
% M. Ratto added lik in output
global bayestopt_ options_
mf = bayestopt_.mf;
smpl = size(Y,2);
mm = size(T,2);
pp = size(Y,1);
a = zeros(mm,1);
dF = 1;
QQ = R*Q*transpose(R);
t = 0;
lik = zeros(smpl,1);
LIK = Inf;
notsteady = 1;
crit = options_.kalman_tol;
while rank(Pinf,crit) & t < smpl
t = t+1;
v = Y(:,t)-a(mf)-trend(:,t);
Finf = Pinf(mf,mf);
if rcond(Finf) < crit
if ~all(abs(Finf(:)) factorization of the transition matrix...
Pstar = T*(Pstar-K*Pstar(mf,:))*transpose(T)+QQ; %% ... idem (stephane)
end
notsteady = ~(max(max(abs(Pstar-oldPstar)))