diff --git a/matlab/missing_DiffuseKalmanSmoother3.m b/matlab/missing_DiffuseKalmanSmoother3.m
deleted file mode 100644
index 664f095adcd661e70185cde8ae209cfd6217ab99..0000000000000000000000000000000000000000
--- a/matlab/missing_DiffuseKalmanSmoother3.m
+++ /dev/null
@@ -1,320 +0,0 @@
-function [alphahat,etahat,a,P,aK,PK,d,decomp] = 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) 2004-2011 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/>.
-
-% 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_
-
-d=0;
-decomp=[];
-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);
-PK = zeros(nk,mm,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);
-Fi = 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);
-for i=1:pp;
- Z(i,mf(i)) = 1;
-end
-
-t = 0;
-icc=0;
-newRank = rank(Pinf(:,:,1),crit1);
-while newRank && t < smpl
- t = t+1;
- a(:,t) = a1(:,t);
- Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
- Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
- Pstar1(:,:,t) = Pstar(:,:,t);
- Pinf1(:,:,t) = Pinf(:,:,t);
- di = data_index{t}';
- for i=di
- v(i,t) = Y(i,t)-a(mf(i),t)-trend(i,t);
- Fstar(i,t) = Pstar(mf(i),mf(i),t);
- Finf(i,t) = Pinf(mf(i),mf(i),t);
- Kstar(:,i,t) = Pstar(:,mf(i),t);
- if Finf(i,t) > crit && newRank, % original MJ: if Finf(i,t) > crit
- icc=icc+1;
- Kinf(:,i,t) = Pinf(:,mf(i),t);
- Linf(:,:,i,t) = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
- L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Z(i,:)/Finf(i,t);
- a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
- Pstar(:,:,t) = Pstar(:,:,t) + ...
- Kinf(:,i,t)*transpose(Kinf(:,i,t))*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - ...
- (Kstar(:,i,t)*transpose(Kinf(:,i,t)) +...
- Kinf(:,i,t)*transpose(Kstar(:,i,t)))/Finf(i,t);
- Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*transpose(Kinf(:,i,t))/Finf(i,t);
- Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
- Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
- % new terminiation criteria by M. Ratto
- P0=Pinf(:,:,t);
- % newRank = any(diag(P0(mf,mf))>crit);
- % if newRank==0, id = i; end,
- if ~isempty(options_.diffuse_d),
- newRank = (icc<options_.diffuse_d);
- %if newRank && any(diag(P0(mf,mf))>crit)==0;
- if newRank && (any(diag(P0(mf,mf))>crit)==0 && rank(P0,crit1)==0);
- disp('WARNING!! Change in OPTIONS_.DIFFUSE_D in univariate DKF')
- options_.diffuse_d = icc;
- newRank=0;
- end
- else
- %newRank = any(diag(P0(mf,mf))>crit);
- newRank = (any(diag(P0(mf,mf))>crit) | rank(P0,crit1));
- if newRank==0,
- options_.diffuse_d = icc;
- end
- end,
- % if newRank==0,
- % options_.diffuse_d=i; %this is buggy
- % end
- % end new terminiation criteria by M. Ratto
- elseif Fstar(i,t) > crit
- %% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
- %% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
- %% rank(Pinf)=0. [st�phane,11-03-2004].
- Li(:,:,i,t) = eye(mm)-Kstar(:,i,t)*Z(i,:)/Fstar(i,t); % we need to store Li for DKF smoother
- a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
- Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*transpose(Kstar(:,i,t))/Fstar(i,t);
- Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
- end
- end
- a1(:,t+1) = T*a(:,t);
- aK(1,:,t+1) = a1(:,t+1);
- for jnk=2:nk
- aK(jnk,:,t+jnk) = T*dynare_squeeze(aK(jnk-1,:,t+jnk-1));
- end
- Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)+ QQ;
- Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T);
- P0=Pinf(:,:,t+1);
- if newRank,
- %newRank = any(diag(P0(mf,mf))>crit);
- newRank = rank(P0,crit1);
- end
-end
-
-
-d = t;
-P(:,:,d+1) = Pstar(:,:,d+1);
-Linf = Linf(:,:,:,1:d);
-L0 = L0(:,:,:,1:d);
-Fstar = Fstar(:,1:d);
-Finf = Finf(:,1:d);
-Kstar = Kstar(:,:,1:d);
-Pstar = Pstar(:,:,1:d);
-Pinf = Pinf(:,:,1:d);
-Pstar1 = Pstar1(:,:,1:d);
-Pinf1 = Pinf1(:,:,1:d);
-notsteady = 1;
-while notsteady && t<smpl
- t = t+1;
- a(:,t) = a1(:,t);
- P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
- P1(:,:,t) = P(:,:,t);
- di = data_index{t}';
- for i=di
- v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
- Fi(i,t) = P(mf(i),mf(i),t);
- Ki(:,i,t) = P(:,mf(i),t);
- if Fi(i,t) > crit
- Li(:,:,i,t) = eye(mm)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
- a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
- P(:,:,t) = P(:,:,t) - Ki(:,i,t)*transpose(Ki(:,i,t))/Fi(i,t);
- P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
- end
- end
- a1(:,t+1) = T*a(:,t);
- Pf = P(:,:,t);
- aK(1,:,t+1) = a1(:,t+1);
- for jnk=1:nk,
- Pf = T*Pf*T' + QQ;
- PK(jnk,:,:,t+jnk) = Pf;
- if jnk>1
- aK(jnk,:,t+jnk) = T*dynare_squeeze(aK(jnk-1,:,t+jnk-1));
- end
- end
- P(:,:,t+1) = T*P(:,:,t)*transpose(T) + QQ;
- % notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<crit);
-end
-% $$$ P_s=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
-% $$$ Fi_s = Fi(:,t);
-% $$$ Ki_s = Ki(:,:,t);
-% $$$ L_s =Li(:,:,:,t);
-% $$$ if t<smpl
-% $$$ t_steady = t+1;
-% $$$ P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
-% $$$ Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t_steady+1]));
-% $$$ Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t_steady+1]));
-% $$$ Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t_steady+1]));
-% $$$ end
-% $$$ while t<smpl
-% $$$ t=t+1;
-% $$$ a(:,t) = a1(:,t);
-% $$$ di = data_index{t}';
-% $$$ for i=di
-% $$$ v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
-% $$$ if Fi_s(i) > crit
-% $$$ a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
-% $$$ end
-% $$$ end
-% $$$ a1(:,t+1) = T*a(:,t);
-% $$$ for jnk=1:nk,
-% $$$ aK(jnk,:,t+jnk) = T^jnk*a(:,t);
-% $$$ end
-% $$$ end
-ri=zeros(mm,1);
-t = smpl+1;
-while t>d+1
- t = t-1;
- di = flipud(data_index{t})';
- for i = di
- if Fi(i,t) > crit
- ri = Z(i,:)'/Fi(i,t)*v(i,t)+Li(:,:,i,t)'*ri;
- end
- end
- r(:,t) = ri;
- alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t);
- etahat(:,t) = QRt*r(:,t);
- ri = T'*ri;
-end
-if d
- r0 = zeros(mm,d);
- r0(:,d) = ri;
- r1 = zeros(mm,d);
- for t = d:-1:1
- di = flipud(data_index{t})';
- for i = di
- if Finf(i,t) > crit && ~(t==d && i>options_.diffuse_d),
- % use of options_.diffuse_d to be sure of DKF termination
- %r1(:,t) = transpose(Z)*v(:,t)/Finf(i,t) + ... BUG HERE in transpose(Z)
- r1(:,t) = Z(i,:)'*v(i,t)/Finf(i,t) + ...
- L0(:,:,i,t)'*r0(:,t) + Linf(:,:,i,t)'*r1(:,t);
- r0(:,t) = Linf(:,:,i,t)'*r0(:,t);
- elseif Fstar(i,t) > crit % step needed whe Finf == 0
- r0(:,t) = Z(i,:)'/Fstar(i,t)*v(i,t)+Li(:,:,i,t)'*r0(:,t);
- end
- end
- alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
- r(:,t) = r0(:,t);
- etahat(:,t) = QRt*r(:,t);
- if t > 1
- r0(:,t-1) = T'*r0(:,t);
- r1(:,t-1) = T'*r1(:,t);
- end
- 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 = zeros(mm,1);
- di = flipud(data_index{t})';
- for i = di
- if Fi(i,t) > crit
- ri_d = Z(i,:)'/Fi(i,t)*v(i,t)+ri_d-Ki(:,i,t)'*ri_d/Fi(i,t)*Z(i,:)';
- end
- end
-
- % calculate eta_tm1t
- eta_tm1t = QRt*ri_d;
- % calculate decomposition
- Ttok = eye(mm,mm);
- AAA = P1(:,:,t)*Z'*ZRQinv*Z*R;
- for h = 1:nk
- BBB = Ttok*AAA;
- for j=1:rr
- decomp(h,:,j,t+h) = eta_tm1t(j)*BBB(:,j);
- end
- Ttok = T*Ttok;
- end
- end
-end