Removed unused routines for (diffuse) kalman filter evaluations.

matlab/DiffuseLikelihood1.m

-function [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,Y,trend,start)
-
-% function [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,Y,trend,start)
-% Computes the diffuse likelihood without 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
-%    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) 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
-
-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(:)) < crit)
-            return
-        else
-            iFstar      = inv(Pstar(mf,mf));
-            dFstar      = det(Pstar(mf,mf));
-            Kstar       = Pstar(:,mf)*iFstar;
-            lik(t)      = log(dFstar) + transpose(v)*iFstar*v;
-            Pinf        = T*Pinf*transpose(T);
-            Pstar       = T*(Pstar-Pstar(:,mf)*transpose(Kstar))*transpose(T)+QQ;
-            a   = T*(a+Kstar*v);
-        end
-    else
-        lik(t)  = log(det(Finf));
-        iFinf   = inv(Finf);
-        Kinf    = Pinf(:,mf)*iFinf;                     %%      premultiplication by the transition matrix T is removed (stephane) 
-        Fstar   = Pstar(mf,mf);
-        Kstar   = (Pstar(:,mf)-Kinf*Fstar)*iFinf;       %%      premultiplication by the transition matrix T is removed (stephane)
-        Pstar   = T*(Pstar-Pstar(:,mf)*transpose(Kinf)-Pinf(:,mf)*transpose(Kstar))*transpose(T)+QQ;
-        Pinf    = T*(Pinf-Pinf(:,mf)*transpose(Kinf))*transpose(T);
-        a               = T*(a+Kinf*v);                                 
-    end  
-end
-if t == smpl                                                           
-    error(['There isn''t enough information to estimate the initial' ... 
-           ' conditions of the nonstationary variables']);                   
-end                                                                    
-F_singular = 1;
-while notsteady & t < smpl
-    t  = t+1;
-    v     = Y(:,t)-a(mf)-trend(:,t);
-    F  = Pstar(mf,mf);
-    oldPstar  = Pstar;
-    dF = det(F);
-    if rcond(F) < crit 
-        if ~all(abs(F(:))<crit)
-            return
-        else
-            a         = T*a;
-            Pstar     = T*Pstar*transpose(T)+QQ;
-        end
-    else
-        F_singular = 0;
-        iF        = inv(F);
-        lik(t)    = log(dF)+transpose(v)*iF*v;
-        K         = Pstar(:,mf)*iF; %% premultiplication by the transition matrix T is removed (stephane)
-        a         = T*(a+K*v);          %% --> factorization of the transition matrix...
-        Pstar     = T*(Pstar-K*Pstar(mf,:))*transpose(T)+QQ;    %% ... idem (stephane)
-    end
-    notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
-end
-if F_singular == 1
-    error(['The variance of the forecast error remains singular until the' ...
-           'end of the sample'])
-end
-if t < smpl
-    t0 = t+1;
-    while t < smpl
-        t = t+1;
-        v = Y(:,t)-a(mf)-trend(:,t);
-        a = T*(a+K*v);
-        lik(t) = transpose(v)*iF*v;
-    end
-    lik(t0:smpl) = lik(t0:smpl) + log(dF);
-end    
-% adding log-likelihhod constants
-lik = (lik + pp*log(2*pi))/2;
-
-LIK = sum(lik(start:end)); % Minus the log-likelihood.
\ No newline at end of file

matlab/DiffuseLikelihood1_Z.m

-function [LIK, lik] = DiffuseLikelihood1_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
-
-% function [LIK, lik] = DiffuseLikelihood1_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
-% Computes the diffuse likelihood without measurement error, in the case of a non-singular var-cov matrix 
-%
-% 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
-
-global bayestopt_ options_
-
-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)-Z*a;
-    Finf  = Z*Pinf*Z';
-    if rcond(Finf) < crit 
-        if ~all(abs(Finf(:)) < crit)
-            return
-        else
-            Fstar   = Z*Pstar*Z';
-            iFstar      = inv(Fstar);
-            dFstar      = det(Fstar);
-            Kstar       = Pstar*Z'*iFstar;
-            lik(t)      = log(dFstar) + v'*iFstar*v;
-            Pinf        = T*Pinf*transpose(T);
-            Pstar       = T*(Pstar-Pstar*Z'*Kstar')*T'+QQ;
-            a   = T*(a+Kstar*v);
-        end
-    else
-        lik(t)  = log(det(Finf));
-        iFinf   = inv(Finf);
-        Kinf    = Pinf*Z'*iFinf;                
-        Fstar   = Z*Pstar*Z';
-        Kstar   = (Pstar*Z'-Kinf*Fstar)*iFinf;  
-        Pstar   = T*(Pstar-Pstar*Z'*Kinf'-Pinf*Z'*Kstar')*T'+QQ;
-        Pinf    = T*(Pinf-Pinf*Z'*Kinf')*T';
-        a               = T*(a+Kinf*v);                                 
-    end  
-end
-if t == smpl                                                           
-    error(['There isn''t enough information to estimate the initial' ... 
-           ' conditions of the nonstationary variables']);                   
-end                                                                    
-F_singular = 1;
-while notsteady & t < smpl
-    t  = t+1;
-    v     = Y(:,t)-Z*a;
-    F  = Z*Pstar*Z';
-    oldPstar  = Pstar;
-    dF = det(F);
-    if rcond(F) < crit 
-        if ~all(abs(F(:))<crit)
-            return
-        else
-            a         = T*a;
-            Pstar     = T*Pstar*T'+QQ;
-        end
-    else
-        F_singular = 0;
-        iF        = inv(F);
-        lik(t)    = log(dF)+v'*iF*v;
-        K         = Pstar*Z'*iF;
-        a         = T*(a+K*v);  
-        Pstar     = T*(Pstar-K*Z*Pstar)*T'+QQ;
-    end
-    notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
-end
-if F_singular == 1
-    error(['The variance of the forecast error remains singular until the' ...
-           'end of the sample'])
-end
-if t < smpl
-    t0 = t+1;
-    while t < smpl
-        t = t+1;
-        v = Y(:,t)-Z*a;
-        a = T*(a+K*v);
-        lik(t) = v'*iF*v;
-    end
-    lik(t0:smpl) = lik(t0:smpl) + log(dF);
-end
-% adding log-likelihhod constants
-lik = (lik + pp*log(2*pi))/2;
-
-LIK = sum(lik(start:end)); % Minus the log-likelihood.
\ No newline at end of file

matlab/DiffuseLikelihood3.m

-function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)%//Z,T,R,Q,Pinf,Pstar,Y)
-
-% function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,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
-%    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
-%    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) 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_
-
-mf = bayestopt_.mf;
-pp     = size(Y,1);
-mm     = size(T,1);
-smpl   = size(Y,2);
-a      = zeros(mm,1);
-QQ     = R*Q*transpose(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
-        v(i)    = Y(i,t)-a(mf(i))-trend(i,t);
-        Fstar   = Pstar(mf(i),mf(i));
-        Finf    = Pinf(mf(i),mf(i));
-        Kstar   = Pstar(:,mf(i));
-        if Finf > crit & newRank,  %added newRank criterion 
-            icc=icc+1;
-            Kinf        = Pinf(:,mf(i));
-            a           = a + Kinf*v(i)/Finf;
-            Pstar       = Pstar + Kinf*transpose(Kinf)*Fstar/(Finf*Finf) - ...
-                (Kstar*transpose(Kinf)+Kinf*transpose(Kstar))/Finf;
-            Pinf        = Pinf - Kinf*transpose(Kinf)/Finf;
-            lik(t)      = lik(t) + log(Finf);
-            % start new termination criterion for DKF
-            if ~isempty(options_.diffuse_d),  
-                newRank = (icc<options_.diffuse_d);  
-                %if newRank & any(diag(Pinf(mf,mf))>crit)==0; %  M. Ratto this line is BUGGY
-                if newRank & (any(diag(Pinf(mf,mf))>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(Pinf(mf,mf))>crit);     % M. Ratto this line is BUGGY 
-                newRank = (any(diag(Pinf(mf,mf))>crit) | rank(Pinf,crit1));                 
-                if newRank==0, 
-                    P0= T*Pinf*transpose(T);
-                    %newRank = any(diag(P0(mf,mf))>crit);   % M. Ratto this line is BUGGY
-                    newRank = (any(diag(P0(mf,mf))>crit) | rank(P0,crit1));   % M. Ratto 11/10/2005
-                    if newRank==0, 
-                        options_.diffuse_d = icc;
-                    end
-                end                    
-            end,
-            % end new termination and checks for DKF
-        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. [stphane,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;
-            %end
-            a   = a + Kstar*v(i)/Fstar;
-            Pstar = Pstar - Kstar*transpose(Kstar)/Fstar;
-        else
-            %disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fstar)])
-        end
-    end 
-    %     if all(abs(Pinf(:))<crit),
-    %         oldRank = 0;
-    %     else
-    %         oldRank = rank(Pinf,crit);
-    %     end
-    if newRank,
-        oldRank = rank(Pinf,crit1);
-    else
-        oldRank = 0;
-    end
-    a           = T*a;
-    Pstar       = T*Pstar*transpose(T)+QQ;
-    Pinf        = T*Pinf*transpose(T);
-    %     if all(abs(Pinf(:))<crit),
-    %         newRank = 0;
-    %     else
-    %           newRank = rank(Pinf,crit);
-    %     end
-    if newRank,
-        newRank = rank(Pinf,crit1);  % new crit1 is used 
-    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
-        v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
-        Fi   = Pstar(mf(i),mf(i));
-        if Fi > crit
-            Ki          = Pstar(:,mf(i));
-            a           = a + Ki*v(i)/Fi;
-            Pstar       = Pstar - Ki*transpose(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*transpose(T) + QQ;
-    notsteady   = ~(max(max(abs(Pstar-oldP)))<crit);
-end
-while t < smpl
-    t = t+1;
-    Pstar = oldP;
-    for i=1:pp
-        v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
-        Fi   = Pstar(mf(i),mf(i));
-        if Fi > crit
-            Ki          = Pstar(:,mf(i));
-            a           = a + Ki*v(i)/Fi;
-            Pstar       = Pstar - Ki*transpose(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.

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. [stphane,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,:);