115 lines
4.0 KiB
Matlab
115 lines
4.0 KiB
Matlab
function [Pstar,Pinf] = compute_Pinf_Pstar(mf,T,R,Q,qz_criterium, restrict_columns)
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% function [Z,ST,QT,R1,Pstar,Pinf] = schur_statespace_transformation(mf,T,R,Q,qz_criterium, restrict_columns)
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% Kitagawa transformation of state space system with a quasi-triangular
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% transition matrix with unit roots at the top, but excluding zero columns of the transition matrix.
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% Computation of Pstar and Pinf for Durbin and Koopman Diffuse filter
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%
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% The transformed state space is
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% y = [ss; z; x];
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% s = static variables (zero columns of T)
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% z = unit roots
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% x = stable roots
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% ss = s - z = stationarized static variables
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%
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% INPUTS
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% mf [integer] vector of indices of observed variables in
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% state vector
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% T [double] matrix of transition
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% R [double] matrix of structural shock effects
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% Q [double] matrix of covariance of structural shocks
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% qz_criterium [double] numerical criterium for unit roots
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%
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% OUTPUTS
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% Pstar [double] matrix of covariance of stationary part
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% Pinf [double] matrix of covariance initialization for
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% nonstationary part
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%
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% ALGORITHM
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% Real Schur transformation of transition equation
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%
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% SPECIAL REQUIREMENTS
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% None
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% Copyright (C) 2006-2017 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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np = size(T,1);
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% perform Kitagawa transformation
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[QT,ST] = schur(T);
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e1 = abs(ordeig(ST)) > 2-qz_criterium;
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[QT,ST] = ordschur(QT,ST,e1);
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k = find(abs(ordeig(ST)) > 2-qz_criterium);
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nk = length(k);
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nk1 = nk+1;
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Pstar = zeros(np,np);
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R1 = QT'*R;
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B = R1*Q*R1';
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% computes variance of stationary block (lower right)
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i = np;
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while i >= nk+2
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if ST(i,i-1) == 0
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if i == np
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c = zeros(np-nk,1);
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else
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c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
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ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
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end
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q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
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Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
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Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
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i = i - 1;
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else
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if i == np
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c = zeros(np-nk,1);
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c1 = zeros(np-nk,1);
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else
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c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
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ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
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ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
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c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
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ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
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ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
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end
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q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
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-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
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z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
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Pstar(nk1:i,i) = z(1:(i-nk));
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Pstar(nk1:i,i-1) = z(i-nk+1:end);
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Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
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Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
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i = i - 2;
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end
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end
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if i == nk+1
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c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
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Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
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end
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% stochastic trends with no influence on observed variables are
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% arbitrarily initialized to zero
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Pinf = zeros(np,np);
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Pinf(1:nk,1:nk) = eye(nk);
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for k = 1:nk
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if norm(QT(mf,:)*ST(:,k)) < 1e-8
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Pinf(k,k) = 0;
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end
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end
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Pinf = QT*Pinf*QT';
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Pstar = QT*Pstar*QT';
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