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088fd64114
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@ -211,10 +211,12 @@ while notsteady && t<smpl
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P(:,:,t+1) = T*P(:,:,t)*L(:,:,t)' + QQ;
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end
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a(:,t+1) = T*atilde(:,t);
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Pf = P(:,:,t);
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Pf = P(:,:,t+1);
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aK(1,:,t+1) = a(:,t+1);
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for jnk=1:nk
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Pf = T*Pf*T' + QQ;
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if jnk>1
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Pf = T*Pf*T' + QQ;
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end
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PK(jnk,:,:,t+jnk) = Pf;
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if jnk>1
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aK(jnk,:,t+jnk) = T*dynare_squeeze(aK(jnk-1,:,t+jnk-1));
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@ -1,5 +1,5 @@
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function [alphahat,epsilonhat,etahat,a,P,aK,PK,decomp,V] = missing_DiffuseKalmanSmootherH3_Z(a_initial,T,Z,R,Q,H,Pinf1,Pstar1,Y,pp,mm,smpl,data_index,nk,kalman_tol,diffuse_kalman_tol,decomp_flag,state_uncertainty_flag)
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% function [alphahat,epsilonhat,etahat,a1,P,aK,PK,d,decomp] = missing_DiffuseKalmanSmootherH3_Z(T,Z,R,Q,H,Pinf1,Pstar1,Y,pp,mm,smpl,data_index,nk,kalman_tol,decomp_flag,state_uncertainty_flag)
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function [alphahat,epsilonhat,etahat,a,P1,aK,PK,decomp,V] = missing_DiffuseKalmanSmootherH3_Z(a_initial,T,Z,R,Q,H,Pinf1,Pstar1,Y,pp,mm,smpl,data_index,nk,kalman_tol,diffuse_kalman_tol,decomp_flag,state_uncertainty_flag)
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% function [alphahat,epsilonhat,etahat,a1,P1,aK,PK,d,decomp] = missing_DiffuseKalmanSmootherH3_Z(T,Z,R,Q,H,Pinf1,Pstar1,Y,pp,mm,smpl,data_index,nk,kalman_tol,decomp_flag,state_uncertainty_flag)
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% Computes the diffuse kalman smoother in the case of a singular var-cov matrix.
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% Univariate treatment of multivariate time series.
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%
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@ -31,7 +31,7 @@ function [alphahat,epsilonhat,etahat,a,P,aK,PK,decomp,V] = missing_DiffuseKalman
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% a: matrix of updated variables (a_{t|t})
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% aK: 3D array of k step ahead filtered state variables (a_{t+k|t})
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% (meaningless for periods 1:d)
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% P: 3D array of one-step ahead forecast error variance
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% P1: 3D array of one-step ahead forecast error variance
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% matrices
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% PK: 4D array of k-step ahead forecast error variance
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% matrices (meaningless for periods 1:d)
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@ -221,18 +221,21 @@ while notsteady && t<smpl
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end
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end
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a1(:,t+1) = T*a(:,t); %transition according to (6.14) in DK (2012)
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Pf = P(:,:,t);
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P(:,:,t+1) = T*P(:,:,t)*T' + QQ; %transition according to (6.14) in DK (2012)
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Pf = P(:,:,t+1);
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aK(1,:,t+1) = a1(:,t+1);
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for jnk=1:nk
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Pf = T*Pf*T' + QQ;
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if jnk>1
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Pf = T*Pf*T' + QQ;
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end
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PK(jnk,:,:,t+jnk) = Pf;
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if jnk>1
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aK(jnk,:,t+jnk) = T*dynare_squeeze(aK(jnk-1,:,t+jnk-1));
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end
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end
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P(:,:,t+1) = T*P(:,:,t)*T' + QQ; %transition according to (6.14) in DK (2012)
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% notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<kalman_tol);
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end
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P1(:,:,t+1) = P(:,:,t+1);
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% $$$ P_s=tril(P(:,:,t))+tril(P(:,:,t),-1)';
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% $$$ P1_s=tril(P1(:,:,t))+tril(P1(:,:,t),-1)';
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% $$$ Fi_s = Fi(:,t);
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@ -34,8 +34,9 @@ function [oo_, yf]=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,da
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% oo_.Smoother.Variance: one-step ahead forecast error variance (declaration order)
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% oo_.Smoother.Constant: structure storing the constant term of the smoother
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% oo_.Smoother.Trend: structure storing the trend term of the smoother
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% oo_.FilteredVariablesKStepAhead: k-step ahead forecast error variance matrices (decision-rule order)
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% oo_.FilteredVariablesShockDecomposition: shock decomposition of k-step ahead filtered variables (decision-rule order)
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% oo_.FilteredVariablesKStepAhead: k-step ahead filtered variables (declaration order)
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% oo_.FilteredVariablesKStepAheadVariances: k-step ahead forecast error variance matrices (declaration order)
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% oo_.FilteredVariablesShockDecomposition: shock decomposition of k-step ahead filtered variables (declaration order)
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% oo_.FilteredVariables: structure storing the filtered variables
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% oo_.UpdatedVariables: structure storing the updated variables
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% oo_.SmoothedShocks: structure storing the smoothed shocks
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