144 lines
4.8 KiB
Matlab
144 lines
4.8 KiB
Matlab
function [LIK, lik] = diffuse_kalman_filter(T,R,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol)
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% Computes the diffuse likelihood of a state space model.
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%
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% INPUTS
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% T [double] mm*mm transition matrix
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% R [double] mm*rr matrix
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% Q [double] rr*rr covariance matrix of the structural innovations.
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% H [double] pp*pp covariance matrix of the measurement errors (if H is equal to zero (scalar) there is no measurement error).
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% Pinf [double] mm*mm matrix used to initialize the covariance matrix of the state vector.
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% Pstar [double] mm*mm matrix used to initialize the covariance matrix of the state vector.
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% Y [double] pp*smpl matrix of (detrended) data, where pp is the number of observed variables.
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% start [integer] scalar, likelihood evaluation starts at 'start'.
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% Z [double] pp*mm matrix, selection matrix or pp linear independant combinations of the state vector.
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% kalman_tol [double] scalar, tolerance parameter (rcond).
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% riccati_tol [double] scalar, tolerance parameter (riccati iteration).
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%
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% OUTPUTS
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% LIK: likelihood
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% lik: density vector in each period
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%
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% REFERENCES
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% See "Filtering and Smoothing of State Vector for Diffuse State Space
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% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
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% Analysis, vol. 24(1), pp. 85-98).
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% Copyright (C) 2004-2008 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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[pp,smpl] = size(Y);
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mm = size(T,2);
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a = zeros(mm,1);
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dF = 1;
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QQ = R*Q*transpose(R);
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t = 0;
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oldK = 0;
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lik = zeros(smpl,1);
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LIK = Inf;
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% lik(smpl+1) = smpl*pp*log(2*pi);
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notsteady = 1;
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reste = 0;
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while rank(Pinf,kalman_tol) && (t<smpl)
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t = t+1;
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v = Y(:,t)-Z*a;
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Finf = Z*Pinf*Z' ;
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if rcond(Finf) < kalman_tol
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if ~all(abs(Finf(:)) < kalman_tol)
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% The univariate diffuse kalman filter should be used.
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return
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else
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Fstar = Z*Pstar*Z' + H;
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if rcond(Fstar) < kalman_tol
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if ~all(abs(Fstar(:))<kalman_tol)
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% The univariate diffuse kalman filter should be used.
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return
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else
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a = T*a;
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Pstar = T*Pstar*transpose(T)+QQ;
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Pinf = T*Pinf*transpose(T);
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end
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else
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iFstar = inv(Fstar);
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dFstar = det(Fstar);
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Kstar = Pstar*Z'*iFstar;
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lik(t) = log(dFstar) + v'*iFstar*v;
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Pinf = T*Pinf*transpose(T);
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Pstar = T*(Pstar-Pstar*Z'*Kstar')*T'+QQ;
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a = T*(a+Kstar*v);
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end
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end
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else
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lik(t) = log(det(Finf));
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iFinf = inv(Finf);
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Kinf = Pinf*Z'*iFinf;
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Fstar = Z*Pstar*Z' + H;
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Kstar = (Pstar*Z'-Kinf*Fstar)*iFinf;
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Pstar = T*(Pstar-Pstar*Z'*Kinf'-Pinf*Z'*Kstar')*T'+QQ;
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Pinf = T*(Pinf-Pinf*Z'*Kinf')*T';
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a = T*(a+Kinf*v);
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end
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end
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if t == smpl
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error(['There isn''t enough information to estimate the initial conditions of the nonstationary variables']);
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end
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F_singular = 1;
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while notsteady && (t<smpl)
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t = t+1;
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v = Y(:,t)-Z*a;
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F = Z*Pstar*Z' + H;
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oldPstar = Pstar;
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dF = det(F);
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if rcond(F) < kalman_tol
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if ~all(abs(F(:))<kalman_tol)
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return
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else
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a = T*a;
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Pstar = T*Pstar*T'+QQ;
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end
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else
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F_singular = 0;
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iF = inv(F);
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lik(t) = log(dF)+v'*iF*v;
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K = Pstar*Z'*iF;
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a = T*(a+K*v);
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Pstar = T*(Pstar-K*Z*Pstar)*T'+QQ;
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end
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notsteady = ~(max(max(abs(K-oldK)))<riccati_tol);
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oldK = K;
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end
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if F_singular == 1
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error(['The variance of the forecast error remains singular until the end of the sample'])
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end
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if t < smpl
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t0 = t;
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while t<smpl
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t = t+1;
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v = Y(:,t)-Z*a;
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a = T*(a+K*v);
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lik(t) = v'*iF*v;
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end
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lik(t0:smpl) = lik(t0:smpl) + log(dF);
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end
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lik = (lik + pp*log(2*pi))/2;
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LIK = sum(lik(start:end)); % Minus the log-likelihood. |