function [alphahat,etahat,atilde,P,aK,PK,d,decomp] = DiffuseKalmanSmoother1_Z(T,Z,R,Q,Pinf1,Pstar1,Y,pp,mm,smpl) % function [alphahat,etahat,a, aK] = DiffuseKalmanSmoother1(T,Z,R,Q,Pinf1,Pstar1,Y,pp,mm,smpl) % Computes the diffuse kalman smoother 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 % Pinf1: mm*mm diagonal matrix with with q ones and m-q zeros % Pstar1: mm*mm variance-covariance matrix with stationary variables % Y: pp*1 vector % pp: number of observed variables % mm: number of state variables % smpl: sample size % % OUTPUTS % alphahat: smoothed variables (a_{t|T}) % etahat: smoothed shocks % atilde: matrix of updated variables (a_{t|t}) % aK: 3D array of k step ahead filtered state variables (a_{t+k|t) % (meaningless for periods 1:d) % P: 3D array of one-step ahead forecast error variance % matrices % PK: 4D array of k-step ahead forecast error variance % matrices (meaningless for periods 1:d) % d: number of periods where filter remains in diffuse part % (should be equal to the order of integration of the model) % decomp: decomposition of the effect of shocks on filtered values % % 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 . % modified by M. Ratto: % new output argument aK (1-step to k-step predictions) % new options_.nk: the max step ahed prediction in aK (default is 4) % new crit1 value for rank of Pinf % it is assured that P is symmetric global options_ d = 0; decomp = []; nk = options_.nk; spinf = size(Pinf1); spstar = size(Pstar1); v = zeros(pp,smpl); a = zeros(mm,smpl+1); atilde = zeros(mm,smpl); aK = zeros(nk,mm,smpl+nk); PK = zeros(nk,mm,mm,smpl+nk); iF = zeros(pp,pp,smpl); Fstar = zeros(pp,pp,smpl); iFinf = zeros(pp,pp,smpl); K = zeros(mm,pp,smpl); L = zeros(mm,mm,smpl); Linf = zeros(mm,mm,smpl); Kstar = zeros(mm,pp,smpl); P = zeros(mm,mm,smpl+1); Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1; Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1; crit = options_.kalman_tol; crit1 = 1.e-8; steady = smpl; rr = size(Q,1); QQ = R*Q*transpose(R); QRt = Q*transpose(R); alphahat = zeros(mm,smpl); etahat = zeros(rr,smpl); r = zeros(mm,smpl+1); t = 0; while rank(Pinf(:,:,t+1),crit1) & td+1 t = t-1; r(:,t) = Z'*iF(:,:,t)*v(:,t) + L(:,:,t)'*r(:,t+1); alphahat(:,t) = a(:,t) + P(:,:,t)*r(:,t); etahat(:,t) = QRt*r(:,t); end if d r0 = zeros(mm,d+1); r0(:,d+1) = r(:,d+1); r1 = zeros(mm,d+1); for t = d:-1:1 r0(:,t) = Linf(:,:,t)'*r0(:,t+1); r1(:,t) = Z'*(iFinf(:,:,t)*v(:,t)-Kstar(:,:,t)'*r0(:,t+1)) + Linf(:,:,t)'*r1(:,t+1); alphahat(:,t) = a(:,t) + Pstar(:,:,t)*r0(:,t) + Pinf(:,:,t)*r1(:,t); etahat(:,t) = QRt*r0(:,t); end end if nargout > 7 decomp = zeros(nk,mm,rr,smpl+nk); ZRQinv = inv(Z*QQ*Z'); for t = max(d,1):smpl ri_d = Z'*iF(:,:,t)*v(:,t); % calculate eta_tm1t eta_tm1t = QRt*ri_d; % calculate decomposition Ttok = eye(mm,mm); for h = 1:nk for j=1:rr eta=zeros(rr,1); eta(j) = eta_tm1t(j); decomp(h,:,j,t+h) = T^(h-1)*P(:,:,t)*Z'*ZRQinv*Z*R*eta; end end end end