correction setting of parameters
git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@446 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
michel
parent
2c14bbc106
commit
b14188d039
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@ -89,7 +89,7 @@ function [fval,cost_flag,ys,trend_coeff] = DsgeLikelihood(xparam1,gend,data)
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offset = offset+estim_params_.ncn;
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end
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for i=1:estim_params_.np
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M_.params(i) = xparam1(i+offset);
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M_.params(estim_params_.param_vals(i,1)) = xparam1(i+offset);
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end
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M_.Sigma_e = Q;
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%------------------------------------------------------------------------------
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@ -2,113 +2,112 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff] = DsgeSmoothe
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% stephane.adjemian@cepremap.cnrs.fr [09-07-2004]
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%
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% Adapted from mj_optmumlik.m
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global bayestopt_ M_ oo_ estim_params_ options_
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global bayestopt_ M_ oo_ estim_params_ options_
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alphahat = [];
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epsilonhat = [];
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etahat = [];
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nobs = size(options_.varobs,1);
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smpl = size(Y,2);
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alphahat = [];
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epsilonhat = [];
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etahat = [];
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nobs = size(options_.varobs,1);
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smpl = size(Y,2);
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Q = M_.Sigma_e;
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for i=1:estim_params_.nvx
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k =estim_params_.var_exo(i,1);
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Q(k,k) = xparam1(i)*xparam1(i);
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end
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offset = estim_params_.nvx;
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if estim_params_.nvn
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H = zeros(nobs,nobs);
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for i=1:estim_params_.nvn
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k = estim_params_.var_endo(i,1);
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H(k,k) = xparam1(i+offset)*xparam1(i+offset);
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end
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end
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offset = offset+estim_params_.nvn;
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for i=1:estim_params_.ncx
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k1 =estim_params_.corrx(i,1);
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k2 =estim_params_.corrx(i,2);
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Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
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Q(k2,k1) = Q(k1,k2);
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end
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offset = offset+estim_params_.ncx;
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Q = M_.Sigma_e;
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for i=1:estim_params_.nvx
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k =estim_params_.var_exo(i,1);
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Q(k,k) = xparam1(i)*xparam1(i);
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end
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offset = estim_params_.nvx;
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if estim_params_.nvn
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H = zeros(nobs,nobs);
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for i=1:estim_params_.nvn
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k = estim_params_.var_endo(i,1);
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H(k,k) = xparam1(i+offset)*xparam1(i+offset);
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end
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end
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offset = offset+estim_params_.nvn;
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for i=1:estim_params_.ncx
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k1 =estim_params_.corrx(i,1);
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k2 =estim_params_.corrx(i,2);
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Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
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Q(k2,k1) = Q(k1,k2);
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end
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offset = offset+estim_params_.ncx;
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if estim_params_.nvn & estim_params_.ncn
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for i=1:estim_params_.ncn
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k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
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k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
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H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
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H(k2,k1) = H(k1,k2);
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end
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offset = offset+estim_params_.ncn;
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end
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for i=1:estim_params_.np
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% assignin('base',deblank(estim_params_.param_names(i,:)),xparam1(i+offset));
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M_.params(i) = xparam1(i+offset);
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end
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M_.Sigma_e = Q;
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%------------------------------------------------------------------------------
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% 2. call model setup & reduction program
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%------------------------------------------------------------------------------
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[T,R,SteadyState] = dynare_resolve;
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if options_.loglinear == 1
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constant = log(SteadyState(bayestopt_.mfys));
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else
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constant = SteadyState(bayestopt_.mfys);
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end
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trend_coeff = zeros(nobs,1);
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if bayestopt_.with_trend == 1
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trend_coeff = zeros(nobs,1);
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nx1 = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn;
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for i=1:nobs
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trend_coeff(i) = evalin('base',bayestopt_.trend_coeff{i});
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end
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trend = constant*ones(1,gend)+trend_coeff*(1:gend);
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else
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trend = constant*ones(1,gend);
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end
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start = options_.presample+1;
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np = size(T,1);
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mf = bayestopt_.mf;
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% ------------------------------------------------------------------------------
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% 3. Initial condition of the Kalman filter
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% ------------------------------------------------------------------------------
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%
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% C'est ici qu'il faut déterminer Pinf et Pstar. Si le modèle est stationnaire,
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% alors il suffit de poser Pstar comme la solution de l'éuation de Lyapounov et
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% Pinf=[].
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%
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if options_.lik_init == 1 % Kalman filter
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Pstar = lyapunov_symm(T,R*Q*transpose(R));
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Pinf = [];
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elseif options_.lik_init == 2 % Old Diffuse Kalman filter
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Pstar = 10*eye(np);
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Pinf = [];
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elseif options_.lik_init == 3 % Diffuse Kalman filter
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Pstar = zeros(np,np);
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ivs = bayestopt_.i_T_var_stable;
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Pstar(ivs,ivs) = lyapunov_symm(T(ivs,ivs),R(ivs,:)*Q* ...
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transpose(R(ivs,:)));
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Pinf = bayestopt_.Pinf;
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end
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% -----------------------------------------------------------------------------
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% 4. Kalman smoother
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% -----------------------------------------------------------------------------
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if estim_params_.nvn
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if options_.kalman_algo == 1
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[alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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if all(alphahat(:)==0)
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[alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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elseif options_.kalman_algo == 3
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[alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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else
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if options_.kalman_algo == 1
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[alphahat,etahat,ahat] = DiffuseKalmanSmoother1(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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if all(alphahat(:)==0)
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[alphahat,etahat,ahat] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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elseif options_.kalman_algo == 3
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[alphahat,etahat,ahat] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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end
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if estim_params_.nvn & estim_params_.ncn
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for i=1:estim_params_.ncn
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k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
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k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
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H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
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H(k2,k1) = H(k1,k2);
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end
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offset = offset+estim_params_.ncn;
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end
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for i=1:estim_params_.np
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M_.params(estim_params_.param_vals(i,1)) = xparam1(i+offset);
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end
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M_.Sigma_e = Q;
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%------------------------------------------------------------------------------
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% 2. call model setup & reduction program
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%------------------------------------------------------------------------------
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[T,R,SteadyState] = dynare_resolve;
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if options_.loglinear == 1
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constant = log(SteadyState(bayestopt_.mfys));
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else
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constant = SteadyState(bayestopt_.mfys);
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end
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trend_coeff = zeros(nobs,1);
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if bayestopt_.with_trend == 1
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trend_coeff = zeros(nobs,1);
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nx1 = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn;
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for i=1:nobs
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trend_coeff(i) = evalin('base',bayestopt_.trend_coeff{i});
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end
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trend = constant*ones(1,gend)+trend_coeff*(1:gend);
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else
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trend = constant*ones(1,gend);
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end
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start = options_.presample+1;
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np = size(T,1);
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mf = bayestopt_.mf;
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% ------------------------------------------------------------------------------
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% 3. Initial condition of the Kalman filter
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% ------------------------------------------------------------------------------
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%
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% C'est ici qu'il faut déterminer Pinf et Pstar. Si le modèle est stationnaire,
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% alors il suffit de poser Pstar comme la solution de l'éuation de Lyapounov et
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% Pinf=[].
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%
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if options_.lik_init == 1 % Kalman filter
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Pstar = lyapunov_symm(T,R*Q*transpose(R));
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Pinf = [];
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elseif options_.lik_init == 2 % Old Diffuse Kalman filter
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Pstar = 10*eye(np);
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Pinf = [];
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elseif options_.lik_init == 3 % Diffuse Kalman filter
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Pstar = zeros(np,np);
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ivs = bayestopt_.i_T_var_stable;
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Pstar(ivs,ivs) = lyapunov_symm(T(ivs,ivs),R(ivs,:)*Q* ...
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transpose(R(ivs,:)));
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Pinf = bayestopt_.Pinf;
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end
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% -----------------------------------------------------------------------------
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% 4. Kalman smoother
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% -----------------------------------------------------------------------------
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if estim_params_.nvn
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if options_.kalman_algo == 1
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[alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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if all(alphahat(:)==0)
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[alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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elseif options_.kalman_algo == 3
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[alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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else
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if options_.kalman_algo == 1
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[alphahat,etahat,ahat] = DiffuseKalmanSmoother1(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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if all(alphahat(:)==0)
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[alphahat,etahat,ahat] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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elseif options_.kalman_algo == 3
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[alphahat,etahat,ahat] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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end
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