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Added new initialization mode ofr the Kalman filter and smoother.

time-shift
Stéphane Adjemian (Charybdis) 2013-06-28 16:26:53 +02:00
parent 664997eec8
commit b281830983
2 changed files with 55 additions and 5 deletions
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33
matlab/DsgeSmoother.m
View File

@ -118,25 +118,50 @@ if options_.lik_init == 1 % Kalman filter
end
Pstar = lyapunov_symm(T,R*Q*transpose(R),options_.qz_criterium,options_.lyapunov_complex_threshold);
Pinf = [];
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
elseif options_.lik_init == 2 % Old Diffuse Kalman filter
if kalman_algo ~= 2
kalman_algo = 1;
end
Pstar = options_.Harvey_scale_factor*eye(np);
Pinf = [];
elseif options_.lik_init == 3 % Diffuse Kalman filter
elseif options_.lik_init == 3 % Diffuse Kalman filter
if kalman_algo ~= 4
kalman_algo = 3;
end
[Z,ST,R1,QT,Pstar,Pinf] = schur_statespace_transformation(mf,T,R,Q,options_.qz_criterium);
elseif options_.lik_init == 4
% Start from the solution of the Riccati equation.
elseif options_.lik_init == 4 % Start from the solution of the Riccati equation.
[err, Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(mf,np,nobs)),H);
mexErrCheck('kalman_steady_state',err);
Pinf = [];
if kalman_algo~=2
kalman_algo = 1;
end
elseif options_.lik_init == 5 % Old diffuse Kalman filter only for the non stationary variables
[eigenvect, eigenv] = eig(T);
eigenv = diag(eigenv);
nstable = length(find(abs(abs(eigenv)-1) > 1e-7));
unstable = find(abs(abs(eigenv)-1) < 1e-7);
V = eigenvect(:,unstable);
indx_unstable = find(sum(abs(V),2)>1e-5);
stable = find(sum(abs(V),2)<1e-5);
nunit = length(eigenv) - nstable;
Pstar = options_.Harvey_scale_factor*eye(np);
if kalman_algo ~= 2
kalman_algo = 1;
end
R_tmp = R(stable, :);
T_tmp = T(stable,stable);
if DynareOptions.lyapunov_fp == 1
Pstar_tmp = lyapunov_symm(T_tmp,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.lyapunov_complex_threshold, 3, R_tmp);
elseif DynareOptions.lyapunov_db == 1
Pstar_tmp = disclyap_fast(T_tmp,R_tmp*Q*R_tmp',DynareOptions.lyapunov_doubling_tol);
elseif DynareOptions.lyapunov_srs == 1
Pstar_tmp = lyapunov_symm(T_tmp,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.lyapunov_complex_threshold, 4, R_tmp);
else
Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold);
end
Pstar(stable, stable) = Pstar_tmp;
Pinf = [];
end
kalman_tol = options_.kalman_tol;
riccati_tol = options_.riccati_tol;

27
matlab/dsge_likelihood.m
View File

@ -373,7 +373,6 @@ switch DynareOptions.lik_init
error(['diffuse filter: options_.kalman_algo can only be equal ' ...
'to 0 (default), 3 or 4'])
end
[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
Zflag = 1;
% Run diffuse kalman filter on first periods.
@ -453,6 +452,32 @@ switch DynareOptions.lik_init
Pinf = [];
a = zeros(mm,1);
Zflag = 0;
elseif options_.lik_init == 5 % Old diffuse Kalman filter only for the non stationary variables
[eigenvect, eigenv] = eig(T);
eigenv = diag(eigenv);
nstable = length(find(abs(abs(eigenv)-1) > 1e-7));
unstable = find(abs(abs(eigenv)-1) < 1e-7);
V = eigenvect(:,unstable);
indx_unstable = find(sum(abs(V),2)>1e-5);
stable = find(sum(abs(V),2)<1e-5);
nunit = length(eigenv) - nstable;
Pstar = options_.Harvey_scale_factor*eye(np);
if kalman_algo ~= 2
kalman_algo = 1;
end
R_tmp = R(stable, :);
T_tmp = T(stable,stable);
if DynareOptions.lyapunov_fp == 1
Pstar_tmp = lyapunov_symm(T_tmp,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.lyapunov_complex_threshold, 3, R_tmp);
elseif DynareOptions.lyapunov_db == 1
Pstar_tmp = disclyap_fast(T_tmp,R_tmp*Q*R_tmp',DynareOptions.lyapunov_doubling_tol);
elseif DynareOptions.lyapunov_srs == 1
Pstar_tmp = lyapunov_symm(T_tmp,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.lyapunov_complex_threshold, 4, R_tmp);
else
Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold);
end
Pstar(stable, stable) = Pstar_tmp;
Pinf = [];
otherwise
error('dsge_likelihood:: Unknown initialization approach for the Kalman filter!')
end