Commit 70665164 authored by michel's avatar michel
Browse files

adding missing data smoother + small corrections

git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@2278 ac1d8469-bf42-47a9-8791-bf33cf982152
parent 6a1c5fbd
......@@ -264,13 +264,25 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,d,
end
else
if kalman_algo == 1
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
if missing_value
[alphahat,etahat,ahat,aK] = missing_DiffuseKalmanSmoother1(T,R,Q, ...
Pinf,Pstar,Y,trend,nobs,np,smpl,mf,data_index);
else
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother1(T,R,Q, ...
Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
end
if all(alphahat(:)==0)
kalman_algo = 2;
end
end
if kalman_algo == 2
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
if missing_value
[alphahat,etahat,ahat,aK] = missing_DiffuseKalmanSmoother3(T,R,Q, ...
Pinf,Pstar,Y,trend,nobs,np,smpl,mf,data_index);
else
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother3(T,R,Q, ...
Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
end
end
if kalman_algo == 3
data1 = Y - trend;
......
......@@ -17,6 +17,6 @@ number_of_observations = length(variable_index);
if ~missing_observations_counter
no_more_missing_observations = 0;
else
tmp = find(missing_observations_counter>=(gend*nvarobs-number_of_observations))
tmp = find(missing_observations_counter>=(gend*nvarobs-number_of_observations));
no_more_missing_observations = tmp(1);
end
\ No newline at end of file
function [alphahat,etahat,atilde, aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf,data_index)
% 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
% data_index [cell] 1*smpl cell of column vectors of indices.
%
% OUTPUTS
% alphahat: smoothed state 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})
% 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/>.
% 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);
atilde = zeros(mm,smpl);
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+1);
Z = zeros(pp,mm);
for i=1:pp;
Z(i,mf(i)) = 1;
end
t = 0;
while rank(Pinf(:,:,t+1),crit1) & t<smpl
t = t+1;
di = data_index{t};
if isempty(di)
atilde(:,t) = a(:,t);
Linf(:,:,t) = T;
Pstar(:,:,t+1) = T*Pstar(:,:,t)*T' + QQ;
Pinf(:,:,t+1) = T*Pinf(:,:,t)*T';
else
mf1 = mf(di);
v(di,t)= Y(di,t) - a(mf1,t) - trend(di,t);
if rcond(Pinf(mf1,mf1,t)) < crit
return
end
iFinf(di,di,t) = inv(Pinf(mf1,mf1,t));
PZI = Pinf(:,mf1,t)*iFinf(di,di,t);
atilde(:,t) = a(:,t) + PZI*v(di,t);
Kinf(:,di,t) = T*PZI;
a(:,t+1) = T*atilde(:,t);
Linf(:,:,t) = T - Kinf(:,di,t)*Z(di,:);
Fstar(di,di,t) = Pstar(mf1,mf1,t);
Kstar(:,di,t) = (T*Pstar(:,mf1,t)-Kinf(:,di,t)*Fstar(di,di,t))*iFinf(di,di,t);
Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)-T*Pstar(:,mf1,t)*transpose(Kinf(:,di,t))-Kinf(:,di,t)*Pinf(mf1,mf1,t)*transpose(Kstar(:,di,t)) + QQ;
Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T)-T*Pinf(:,mf1,t)* ...
transpose(Kinf(:,di,t));
end
aK(1,:,t+1) = a(:,t+1);
for jnk=2:nk,
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
end
end
d = t;
P(:,:,d+1) = Pstar(:,:,d+1);
iFinf = iFinf(:,:,1:d);
Linf = Linf(:,:,1:d);
Fstar = Fstar(:,:,1:d);
Kstar = Kstar(:,:,1:d);
Pstar = Pstar(:,:,1:d);
Pinf = Pinf(:,:,1:d);
notsteady = 1;
while notsteady & t<smpl
t = t+1;
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
di = data_index{t};
if isempty(di)
atilde(:,t) = a(:,t);
L(:,:,t) = T;
P(:,:,t+1) = T*P(:,:,t)*T' + QQ;
else
mf1 = mf(di);
v(di,t) = Y(di,t) - a(mf1,t) - trend(di,t);
if rcond(P(mf1,mf1,t)) < crit
return
end
iF(di,di,t) = inv(P(mf1,mf1,t));
PZI = P(:,mf1,t)*iF(di,di,t);
atilde(:,t) = a(:,t) + PZI*v(di,t);
K(:,di,t) = T*PZI;
L(:,:,t) = T-K(:,di,t)*Z(di,:);
a(:,t+1) = T*atilde(:,t);
end
aK(1,:,t+1) = a(:,t+1);
for jnk=2:nk,
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
end
P(:,:,t+1) = T*P(:,:,t)*transpose(T)-T*P(:,mf,t)*transpose(K(:,:,t)) + QQ;
% notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
end
% $$$ if t<smpl
% $$$ PZI_s = PZI;
% $$$ K_s = K(:,:,t);
% $$$ iF_s = iF(:,:,t);
% $$$ P_s = P(:,:,t+1);
% $$$ t_steady = t+1;
% $$$ P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
% $$$ iF = cat(3,iF(:,:,1:t),repmat(inv(P_s(mf,mf)),[1 1 smpl-t_steady+1]));
% $$$ L = cat(3,L(:,:,1:t),repmat(T-K_s*Z,[1 1 smpl-t_steady+1]));
% $$$ K = cat(3,K(:,:,1:t),repmat(T*P_s(:,mf)*iF_s,[1 1 smpl-t_steady+1]));
% $$$ end
% $$$ while t<smpl
% $$$ t=t+1;
% $$$ v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
% $$$ atilde(:,t) = a(:,t) + PZI*v(:,t);
% $$$ a(:,t+1) = T*a(:,t) + K_s*v(:,t);
% $$$ aK(1,:,t+1) = a(:,t+1);
% $$$ for jnk=2:nk,
% $$$ aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
% $$$ end
% $$$ end
t = smpl+1;
while t>d+1
t = t-1;
di = data_index{t};
if isempty(di)
r(:,t) = L(:,:,t)'*r(:,t+1);
else
r(:,t) = Z(di,:)'*iF(di,di,t)*v(di,t) + L(:,:,t)'*r(:,t+1);
end
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);
di = data_index{t};
if isempty(di)
r1(:,t) = Linf(:,:,t)'*r1(:,t+1);
else
r1(:,t) = Z(di,:)'*(iFinf(di,di,t)*v(di,t)-Kstar(:,di,t)'*r0(:,t+1)) ...
+ Linf(:,:,t)'*r1(:,t+1);
end
alphahat(:,t) = a(:,t) + Pstar(:,:,t)*r0(:,t) + Pinf(:,:,t)*r1(:,t);
etahat(:,t) = QRt*r0(:,t);
end
end
function [alphahat,etahat,atilde,P,aK,PK,d,decomp] = DiffuseKalmanSmoother1_Z(T,Z,R,Q,Pinf1,Pstar1,Y,pp,mm,smpl,data_index)
% 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
% data_index [cell] 1*smpl cell of column vectors of indices.
%
% 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 <http://www.gnu.org/licenses/>.
% 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) & t<smpl
t = t+1;
di = data_index{t};
if isempty(di)
atilde(:,t) = a(:,t);
Linf(:,:,t) = T;
Pstar(:,:,t+1) = T*Pstar(:,:,t)*T' + QQ;
Pinf(:,:,t+1) = T*Pinf(:,:,t)*T';
else
ZZ = Z(di,:);
v(di,t)= Y(di,t) - ZZ*a(:,t);
F = ZZ*Pinf(:,:,t)*ZZ';
if rcond(F) < crit
return
end
iFinf(di,di,t) = inv(F);
PZI = Pinf(:,:,t)*ZZ'*iFinf(di,di,t);
atilde(:,t) = a(:,t) + PZI*v(di,t);
Kinf(:,di,t) = T*PZI;
Linf(:,:,t) = T - Kinf(:,di,t)*ZZ;
Fstar(di,di,t) = ZZ*Pstar(:,:,t)*ZZ';
Kstar(:,di,t) = (T*Pstar(:,:,t)*ZZ'-Kinf(:,di,t)*Fstar(di,di,t))*iFinf(di,di,t);
Pstar(:,:,t+1) = T*Pstar(:,:,t)*T'-T*Pstar(:,:,t)*ZZ'*Kinf(:,di,t)'-Kinf(:,di,t)*F*Kstar(:,di,t)' + QQ;
Pinf(:,:,t+1) = T*Pinf(:,:,t)*T'-T*Pinf(:,:,t)*ZZ'*Kinf(:,di,t)';
end
a(:,t+1) = T*atilde(:,t);
aK(1,:,t+1) = a(:,t+1);
% isn't a meaningless as long as we are in the diffuse part? MJ
for jnk=2:nk,
aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
end
end
d = t;
P(:,:,d+1) = Pstar(:,:,d+1);
iFinf = iFinf(:,:,1:d);
Linf = Linf(:,:,1:d);
Fstar = Fstar(:,:,1:d);
Kstar = Kstar(:,:,1:d);
Pstar = Pstar(:,:,1:d);
Pinf = Pinf(:,:,1:d);
notsteady = 1;
while notsteady & t<smpl
t = t+1;
P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
di = data_index{t};
if isempty(di)
atilde(:,t) = a(:,t);
L(:,:,t) = T;
P(:,:,t+1) = T*P(:,:,t)*T' + QQ;
else
ZZ = Z(di,:);
v(di,t) = Y(di,t) - ZZ*a(:,t);
F = ZZ*P(:,:,t)*ZZ';
if rcond(F) < crit
return
end
iF(di,di,t) = inv(F);
PZI = P(:,:,t)*ZZ'*iF(di,di,t);
atilde(:,t) = a(:,t) + PZI*v(di,t);
K(:,di,t) = T*PZI;
L(:,:,t) = T-K(:,di,t)*ZZ;
P(:,:,t+1) = T*P(:,:,t)*T'-T*P(:,:,t)*ZZ'*K(:,di,t)' + QQ;
end
a(:,t+1) = T*atilde(:,t);
Pf = P(:,:,t);
for jnk=1:nk,
Pf = T*Pf*T' + QQ;
aK(jnk,:,t+jnk) = T^jnk*atilde(:,t);
PK(jnk,:,:,t+jnk) = Pf;
end
% notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
end
% $$$ if t<smpl
% $$$ PZI_s = PZI;
% $$$ K_s = K(:,:,t);
% $$$ iF_s = iF(:,:,t);
% $$$ P_s = P(:,:,t+1);
% $$$ P = cat(3,P(:,:,1:t),repmat(P_s,[1 1 smpl-t]));
% $$$ iF = cat(3,iF(:,:,1:t),repmat(iF_s,[1 1 smpl-t]));
% $$$ L = cat(3,L(:,:,1:t),repmat(T-K_s*Z,[1 1 smpl-t]));
% $$$ K = cat(3,K(:,:,1:t),repmat(T*P_s*Z'*iF_s,[1 1 smpl-t]));
% $$$ end
% $$$ while t<smpl
% $$$ t=t+1;
% $$$ v(:,t) = Y(:,t) - Z*a(:,t);
% $$$ atilde(:,t) = a(:,t) + PZI*v(:,t);
% $$$ a(:,t+1) = T*atilde(:,t);
% $$$ Pf = P(:,:,t);
% $$$ for jnk=1:nk,
% $$$ Pf = T*Pf*T' + QQ;
% $$$ aK(jnk,:,t+jnk) = T^jnk*atilde(:,t);
% $$$ PK(jnk,:,:,t+jnk) = Pf;
% $$$ end
% $$$ end
t = smpl+1;
while t>d+1
t = t-1;
di = data_index{t};
if isempty(di)
r(:,t) = L(:,:,t)'*r(:,t+1);
else
ZZ = Z(di,:);
r(:,t) = ZZ'*iF(di,di,t)*v(di,t) + L(:,:,t)'*r(:,t+1);
end
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);
di = data_index{t};
if isempty(di)
r1(:,t) = Linf(:,:,t)'*r1(:,t+1);
else
r1(:,t) = Z(di,:)'*(iFinf(di,di,t)*v(di,t)-Kstar(:,di,t)'*r0(:,t+1)) ...
+ Linf(:,:,t)'*r1(:,t+1);
end
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
function [alphahat,etahat,a, aK] = missing_DiffuseKalmanSmoother3(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf,data_index)
% function [alphahat,etahat,a1, aK] = missing_DiffuseKalmanSmoother3(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf,data_index)
% Computes the diffuse kalman smoother 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
% 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
% data_index [cell] 1*smpl cell of column vectors of indices.
%
% OUTPUTS
% alphahat: smoothed state variables (a_{t|T})
% etahat: smoothed shocks
% a: matrix of updated variables (a_{t|t})
% aK: 3D array of k step ahead filtered state variables (a_{t+k|t})
%
% 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/>.
% Modified by M. Ratto
% New output argument aK: 1-step to nk-stpe ahed predictions)
% New input argument nk: max order of predictions in aK
% New option options_.diffuse_d where the DKF stops (common with
% diffuselikelihood3)
% New icc variable to count number of iterations for Finf steps
% Pstar % Pinf simmetric
% New termination of DKF iterations based on options_.diffuse_d
% Li also stored during DKF iterations !!
% some bugs corrected in the DKF part of the smoother (Z matrix and
% alphahat)
global options_
nk = options_.nk;
spinf = size(Pinf1);
spstar = size(Pstar1);
v = zeros(pp,smpl);
a = zeros(mm,smpl);
a1 = zeros(mm,smpl+1);
aK = zeros(nk,mm,smpl+nk);
if isempty(options_.diffuse_d),
smpl_diff = 1;
else
smpl_diff=rank(Pinf1);
end
Fstar = zeros(pp,smpl_diff);
Finf = zeros(pp,smpl_diff);
Ki = zeros(mm,pp,smpl);
Li = zeros(mm,mm,pp,smpl);
Linf = zeros(mm,mm,pp,smpl_diff);
L0 = zeros(mm,mm,pp,smpl_diff);
Kstar = zeros(mm,pp,smpl_diff);
P = zeros(mm,mm,smpl+1);
P1 = P;
Pstar = zeros(spstar(1),spstar(2),smpl_diff+1); Pstar(:,:,1) = Pstar1;
Pinf = zeros(spinf(1),spinf(2),smpl_diff+1); Pinf(:,:,1) = Pinf1;
Pstar1 = Pstar;
Pinf1 = Pinf;
crit = options_.kalman_tol;
crit1 = 1.e-6;
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);
Z = zeros(pp,mm);