function [alphahat,etahat,a, aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)
% function [alphahat,etahat,a, aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)
% Computes the diffuse kalman smoother 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
% 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
% trend
% pp: number of observed variables
% mm: number of state variables
% smpl: sample size
% mf: observed variables index in the state vector
%
% OUTPUTS
% alphahat: smoothed state variables
% etahat: smoothed shocks
% a: matrix of one step ahead filtered state variables
% aK: 3D array of k step ahead filtered state variables
% 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_
nk = options_.nk;
spinf = size(Pinf1);
spstar = size(Pstar1);
v = zeros(pp,smpl);
a = zeros(mm,smpl+1);
aK = zeros(nk,mm,smpl+1);
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);
Z = zeros(pp,mm);
for i=1:pp;
Z(i,mf(i)) = 1;
end
t = 0;
while rank(Pinf(:,:,t+1),crit1) & td+1 & t>2
t = t-1;
r(:,t-1) = transpose(Z)*iF(:,:,t)*v(:,t) + transpose(L(:,:,t))*r(:,t);
alphahat(:,t) = a(:,t) + P(:,:,t)*r(:,t-1);
etahat(:,t) = QRt*r(:,t);
end
if d
r0 = zeros(mm,d); r0(:,d) = r(:,d);
r1 = zeros(mm,d);
for t = d:-1:2
r0(:,t-1) = transpose(Linf(:,:,t))*r0(:,t);
r1(:,t-1) = transpose(Z)*(iFinf(:,:,t)*v(:,t)-transpose(Kstar(:,:,t))*r0(:,t)) + transpose(Linf(:,:,t))*r1(:,t);
alphahat(:,t) = a(:,t) + Pstar(:,:,t)*r0(:,t-1) + Pinf(:,:,t)*r1(:,t-1);
etahat(:,t) = QRt*r0(:,t);
end
r0_0 = transpose(Linf(:,:,1))*r0(:,1);
r1_0 = transpose(Z)*(iFinf(:,:,1)*v(:,1)-transpose(Kstar(:,:,1))*r0(:,1)) + transpose(Linf(:,:,1))*r1(:,1);
alphahat(:,1) = a(:,1) + Pstar(:,:,1)*r0_0 + Pinf(:,:,1)*r1_0;
etahat(:,1) = QRt*r0(:,1);
else
r0 = transpose(Z)*iF(:,:,1)*v(:,1) + transpose(L(:,:,1))*r(:,1);
alphahat(:,1) = a(:,1) + P(:,:,1)*r0;
etahat(:,1) = QRt*r(:,1);
end