dynare/matlab/DiffuseLikelihood3_Z.m

function [LIK, lik] = DiffuseLikelihood3_Z(T,Z,R,Q,Pinf,Pstar,Y,start)

% function [LIK, lik] = DiffuseLikelihood3_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
% Computes the diffuse likelihood without measurement error, in the case of
% a singular var-cov matrix.
% Univariate treatment of multivariate time series.
%
% INPUTS
%    T:      mm*mm matrix
%    Z:      pp*mm matrix  
%    R:      mm*rr matrix
%    Q:      rr*rr 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
%    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) 2004-2008 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 <http://www.gnu.org/licenses/>.

% M. Ratto added lik in output [October 2005]
% changes by M. Ratto [April 2005]
% introduced new options options_.diffuse_d  for termination of DKF
% new icc counter for Finf steps in DKF
% new termination for DKF
% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
% zero. 
% [4/5/2005] correctyed bug in the modified verson of Ratto for rank of Pinf 
% introduced a specific crit1 for the DKF termination


global bayestopt_ options_

pp     = size(Y,1);
mm     = size(T,1);
smpl   = size(Y,2);
a      = zeros(mm,1);
QQ     = R*Q*R';
t      = 0;
lik	   = zeros(smpl,1);	
notsteady 	= 1;
crit      	= options_.kalman_tol;
crit1      	= 1.e-6;
newRank	 	= rank(Pinf,crit1);
icc=0;
while newRank & t < smpl
  t = t+1;
  for i=1:pp
    Zi          = Z(i,:);
    v(i) 	= Y(i,t)-Zi*a;
    Fstar 	= Zi*Pstar*Zi';
    Finf	= Zi*Pinf*Zi';
    Kstar 	= Pstar*Zi';
    if Finf > crit & newRank
      icc=icc+1;
      Kinf	= Pinf*Zi';
      a		= a + Kinf*v(i)/Finf;
      Pstar	= Pstar + Kinf*Kinf'*Fstar/(Finf*Finf) - ...
	  (Kstar*Kinf'+Kinf*Kstar')/Finf;
      Pinf	= Pinf - Kinf*Kinf'/Finf;
      lik(t) 	= lik(t) + log(Finf);
      if ~isempty(options_.diffuse_d),  
	newRank = (icc<options_.diffuse_d);  
	if newRank & (any(diag(Z*Pinf*Z')>crit)==0 & rank(Pinf,crit1)==0); 
	  options_.diffuse_d = icc;
	  newRank=0;
	  disp('WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF')
	  disp(['new OPTIONS_.DIFFUSE_D = ',int2str(icc)])
	  disp('You may have to reset the optimisation')
	end
      else
	newRank = (any(diag(Z*Pinf*Z')>crit) | rank(Pinf,crit1));                 
	if newRank==0, 
	  P0=	T*Pinf*T';
	  newRank = (any(diag(Z*P0*Z')>crit) | rank(P0,crit1));   % M. Ratto 11/10/2005
	  if newRank==0, 
	    options_.diffuse_d = icc;
	  end
	end                    
      end,
    elseif Fstar > crit 
      %% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
      %% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
      %% rank(Pinf)=0. [st<73>phane,11-03-2004].	  
      %if rank(Pinf,crit) == 0
      % the likelihood terms should alwasy be cumulated, not only
      % when Pinf=0, otherwise the lik would depend on the ordering
      % of observed variables
      % presample options can be used to ignore initial time points
      lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
      a	= a + Kstar*v(i)/Fstar;
      Pstar = Pstar - Kstar*(Kstar'/Fstar);
    else
      %disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fstar)])
    end
  end 
    if newRank,
        oldRank = rank(Pinf,crit1);
    else
        oldRank = 0;
    end
	a 		= T*a;
	Pstar 	= T*Pstar*T'+QQ;
	Pinf	= T*Pinf*T';
    if newRank,
        newRank = rank(Pinf,crit1);
    end
	if oldRank ~= newRank
		disp('DiffuseLiklihood3 :: T does influence the rank of Pinf!')	
	end  
end
if t == smpl                                                           
  error(['There isn''t enough information to estimate the initial' ... 
	 ' conditions of the nonstationary variables']);                   
end   
while notsteady & t < smpl
  t = t+1;
  oldP = Pstar;
  for i=1:pp
    Zi = Z(i,:);
    v(i) = Y(i,t) - Zi*a;
    Fi   = Zi*Pstar*Zi';
    if Fi > crit
      Ki	= Pstar*Zi';
      a		= a + Ki*v(i)/Fi;
      Pstar 	= Pstar - Ki*(Ki'/Fi);
      lik(t) 	= lik(t) + log(Fi) + v(i)*v(i)/Fi;
    else
      %disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
    end
  end	
  a 			= T*a;
  Pstar 		= T*Pstar*T' + QQ;
  notsteady 	= ~(max(max(abs(Pstar-oldP)))<crit);
end
while t < smpl
  t = t+1;
  Pstar = oldP;
  for i=1:pp
    Zi = Z(i,:);
    v(i) = Y(i,t) - Zi*a;
    Fi   = Zi*Pstar*Zi';
    if Fi > crit
      Ki 		= Pstar*Zi';
      a 		= a + Ki*v(i)/Fi;
      Pstar 	= Pstar - Ki*Ki'/Fi;
      lik(t)    	= lik(t) + log(Fi) + v(i)*v(i)/Fi;
    else
      %disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
    end
  end	
  a = T*a;
end
% adding log-likelihhod constants
lik = (lik + pp*log(2*pi))/2;

LIK = sum(lik(start:end)); % Minus the log-likelihood.