function [dLIK,dlik,a,Pstar] = kalman_filter_d(Y, start, last, a, Pinf, Pstar, kalman_tol, riccati_tol, presample, T, R, Q, H, Z, mm, pp, rr)
% Computes the diffuse likelihood of a state space model.
%
% INPUTS 
%    Y           [double]      pp*smpl matrix of (detrended) data, where pp is the number of observed variables.
%    start       [integer]     scalar, first observation.
%    last        [integer]     scalar, last observation.
%    a           [double]      mm*1 vector, levels of the state variables.
%    Pinf        [double]      mm*mm matrix used to initialize the covariance matrix of the state vector.
%    Pstar       [double]      mm*mm matrix used to initialize the covariance matrix of the state vector.
%    kalman_tol  [double]      scalar, tolerance parameter (rcond).
%    riccati_tol [double]      scalar, tolerance parameter (riccati iteration).
%    presample   [integer]     scalar, presampling if strictly positive.    
%    T           [double]      mm*mm matrix, transition matrix in  the state equations.
%    R           [double]      mm*rr matrix relating the structural innovations to the state vector.
%    Q           [double]      rr*rr covariance matrix of the structural innovations.
%    H           [double]      pp*pp covariance matrix of the measurement errors (if H is equal to zero (scalar) there is no measurement error). 
%    Z           [double]      pp*mm matrix, selection matrix or pp linear independant combinations of the state vector.
%    mm          [integer]     scalar, number of state variables.
%    pp          [integer]     scalar, number of observed variables.
%    rr          [integer]     scalar, number of structural innovations.    
%             
% OUTPUTS 
%    LIK         [double]      scalar, minus loglikelihood
%    lik         [double]      smpl*1 vector, log density of each vector of observations.
%    a           [double]      mm*1 vector, current estimate of the state vector.
%    Pstar       [double]      mm*mm matrix, covariance matrix of the state vector.    
%        
% REFERENCES 
%   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) 2004-2011 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/>.

% Get sample size.
smpl = last-start+1;

% Initialize some variables.
dF   = 1;
QQ   = R*Q*transpose(R);   % Variance of R times the vector of structural innovations.
t    = start;              % Initialization of the time index.
dlik = zeros(smpl,1);      % Initialization of the vector gathering the densities.
dLIK = Inf;                % Default value of the log likelihood.
oldK = Inf;

while rank(Pinf,kalman_tol) && (t<=last)
    s = t-start+1;
    v = Y(:,t)-Z*a;
    Finf  = Z*Pinf*Z';
    if rcond(Finf) < kalman_tol
        if ~all(abs(Finf(:)) < kalman_tol)
            % The univariate diffuse kalman filter should be used instead.
            return
        else
            Fstar  = Z*Pstar*Z' + H;
            if rcond(Fstar) < kalman_tol
                if ~all(abs(Fstar(:))<kalman_tol)
                    % The univariate diffuse kalman filter should be used.
                    return
                else
                    a = T*a;
                    Pstar = T*Pstar*transpose(T)+QQ;
                    Pinf  = T*Pinf*transpose(T);
                end
            else
                iFstar = inv(Fstar);
                dFstar = det(Fstar);
                Kstar  = Pstar*Z'*iFstar;
                dlik(s)= log(dFstar) + v'*iFstar*v;
                Pinf   = T*Pinf*transpose(T);
                Pstar  = T*(Pstar-Pstar*Z'*Kstar')*T'+QQ;
                a      = T*(a+Kstar*v);
            end
        end
    else
        dlik(s)= log(det(Finf));
        iFinf  = inv(Finf);
        Kinf   = Pinf*Z'*iFinf;
        Fstar  = Z*Pstar*Z' + H;
        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
    t = t+1;
end

if t>last
    warning(['There isn''t enough information to estimate the initial conditions of the nonstationary variables']);                   
    dLIK = NaN;
    return
end

dlik = dlik(1:s);
dlik = .5*(dlik + pp*log(2*pi));

if presample 
    if presample>=length(dlik)
        dLIK = 0;
        dlik = [];
    else
        dlik = dlik(1+presample:end);
        dLIK = sum(dlik);% Minus the log-likelihood (for the initial periods).
    end
else
    dLIK = sum(dlik);
end