1) Extended optimizer = 5 for analytic derivatives;
2) Start adapting identification routines to allow computation of analytic asymptotic Hessian with KF routinestime-shift
Marco Ratto
parent
45bc5c2459
commit
2fecf9946b
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@ -50,7 +50,7 @@ end
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% DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
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% end
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while notsteady & t<smpl
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while notsteady && t<smpl
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t = t+1;
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v = Y(:,t)-a(mf);
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F = P(mf,mf) + H;
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@ -112,7 +112,7 @@ end
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a = T*(a+K*v);
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lik(t) = transpose(v)*iF*v;
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end
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AHess = AHess + .5*(smpl+t0-1)*(vecDPmf' * kron(iF,iF) * vecDPmf);
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AHess = AHess + .5*(smpl-t0+1)*(vecDPmf' * kron(iF,iF) * vecDPmf);
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if nargout > 1
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for ii = 1:k
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% DLIK(ii,1) = DLIK(ii,1) + (smpl-t0+1)*trace( iF*DF(:,:,ii) );
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@ -332,7 +332,7 @@ if (kalman_algo == 2) || (kalman_algo == 4)
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else
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if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
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H = diag(H);
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mmm = mm;
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mmm = mm;
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else
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Z = [Z, eye(pp)];
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T = blkdiag(T,zeros(pp));
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@ -381,7 +381,7 @@ switch DynareOptions.lik_init
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% Use standard kalman filter except if the univariate filter is explicitely choosen.
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if kalman_algo == 0
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kalman_algo = 3;
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elseif ~((kalman_algo == 3) || (kalman_algo == 4))
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elseif ~((kalman_algo == 3) || (kalman_algo == 4))
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error(['diffuse filter: options_.kalman_algo can only be equal ' ...
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'to 0 (default), 3 or 4'])
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end
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@ -455,7 +455,7 @@ switch DynareOptions.lik_init
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DynareOptions.lik_init = 1;
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Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold);
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end
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Pinf = [];
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Pinf = [];
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a = zeros(mm,1);
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Zflag = 0;
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otherwise
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@ -471,9 +471,13 @@ if analytic_derivation,
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no_DLIK = 0;
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full_Hess = analytic_derivation==2;
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asy_Hess = analytic_derivation==-2;
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outer_product_gradient = analytic_derivation==-1;
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if asy_Hess,
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analytic_derivation=1;
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end
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if outer_product_gradient,
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analytic_derivation=1;
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end
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DLIK = [];
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AHess = [];
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if nargin<8 || isempty(derivatives_info)
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@ -578,7 +582,7 @@ if analytic_derivation,
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end
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end
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if analytic_derivation==1,
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analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP};
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analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,asy_Hess};
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else
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analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P};
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end
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@ -614,6 +618,8 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
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if analytic_derivation,
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LIK1=LIK;
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LIK=LIK1{1};
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lik1=lik;
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lik=lik1{1};
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end
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if isinf(LIK)
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if kalman_algo == 1
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@ -676,6 +682,8 @@ if (kalman_algo==2) || (kalman_algo==4)
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if analytic_derivation,
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LIK1=LIK;
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LIK=LIK1{1};
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lik1=lik;
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lik=lik1{1};
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end
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if DynareOptions.lik_init==3
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LIK = LIK+dLIK;
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@ -690,13 +698,17 @@ if analytic_derivation
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DLIK = LIK1{2};
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% [DLIK] = score(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,start,Z,kalman_tol,riccati_tol);
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end
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if full_Hess,
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if full_Hess ,
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Hess = -LIK1{3};
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% [Hess, DLL] = get_Hessian(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P,start,Z,kalman_tol,riccati_tol);
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% Hess0 = getHessian(Y,T,DT,D2T, R*Q*transpose(R),DOm,D2Om,Z,DYss,D2Yss);
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end
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if asy_Hess,
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[Hess] = AHessian(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,start,Z,kalman_tol,riccati_tol);
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if ~((kalman_algo==2) || (kalman_algo==4)),
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[Hess] = AHessian(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,start,Z,kalman_tol,riccati_tol);
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else
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Hess = LIK1{3};
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end
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end
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end
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@ -725,6 +737,11 @@ if analytic_derivation
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if no_DLIK==0
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DLIK = DLIK - dlnprior';
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end
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if outer_product_gradient,
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dlik = lik1{2};
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dlik=[- dlnprior; dlik(start:end,:)];
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Hess = dlik'*dlik;
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end
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else
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lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
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end
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@ -213,22 +213,29 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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else
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flag = 1;
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end
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if ~exist('igg','var'), % by M. Ratto
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hh=[];
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gg=[];
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igg=[];
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end % by M. Ratto
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if isfield(options_,'ftol')
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crit = options_.ftol;
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else
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crit = 1.e-5;
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end
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if options_.analytic_derivation,
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analytic_grad=1;
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ana_deriv = options_.analytic_derivation;
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options_.analytic_derivation = -1;
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crit = 1.e-7;
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flag = 0;
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else
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analytic_grad=0;
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end
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if isfield(options_,'nit')
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nit = options_.nit;
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else
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nit=1000;
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end
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[xparam1,hh,gg,fval,invhess] = newrat(objective_function,xparam1,hh,gg,igg,crit,nit,flag,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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[xparam1,hh,gg,fval,invhess] = newrat(objective_function,xparam1,analytic_grad,crit,nit,flag,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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if options_.analytic_derivation,
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options_.analytic_derivation = ana_deriv;
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end
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parameter_names = bayestopt_.name;
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save([M_.fname '_mode.mat'],'xparam1','hh','gg','fval','invhess','parameter_names');
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case 6
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@ -123,10 +123,13 @@ if isempty(dataset_),
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dataset_.info.varobs = options_.varobs;
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dataset_.rawdata = [];
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dataset_.missing.state = 0;
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dataset_.missing.aindex = [];
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for jdata=1:periods,
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temp1{jdata}=[1:dataset_.info.nvobs]';
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end
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dataset_.missing.aindex = temp1;
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dataset_.missing.vindex = [];
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dataset_.missing.number_of_observations = [];
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dataset_.missing.no_more_missing_observations = [];
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dataset_.missing.no_more_missing_observations = 1;
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dataset_.descriptive.mean = [];
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dataset_.data = [];
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@ -7,7 +7,7 @@ function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info] = i
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% o indx [array] index of estimated parameters
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% o indexo [array] index of estimated shocks
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% o options_ident [structure] identification options
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% o data_info [structure] data info for Kalmna Filter
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% o data_info [structure] data info for Kalman Filter
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% o prior_exist [integer]
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% =1 when prior exists and indentification is checked only for estimated params and shocks
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% =0 when prior is not defined and indentification is checked for all params and shocks
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@ -24,18 +24,18 @@ persistent DK DF D2K D2F
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if notsteady
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if Zflag
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[DK,DF,DP1] = computeDKalmanZ(T,DT,DOm,P,DP,DH,Z,iF,K);
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if nargout>3,
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if nargout>4,
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[D2K,D2F,D2P1] = computeD2KalmanZ(T,DT,D2T,D2Om,P,DP,D2P,DH,Z,iF,K,DK);
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end
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else
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[DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,Z,iF,K);
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if nargout>3,
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if nargout>4,
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[D2K,D2F,D2P1] = computeD2Kalman(T,DT,D2T,D2Om,P,DP,D2P,DH,Z,iF,K,DK);
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end
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end
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else
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DP1=DP;
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if nargout>3,
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if nargout>4,
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D2P1=D2P;
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end
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end
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@ -95,10 +95,27 @@ for ii = 1:k
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end
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end
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end
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Da(:,ii) = DT(:,:,ii)*tmp + T*dtmp(:,ii);
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DLIK(ii,1) = trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
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end
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if nargout==4,
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% Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),0,iF,diFi,diFj,0,dFj,0);
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vecDPmf = reshape(DF,[],k);
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D2a = 2*Dv'*iF*Dv + (vecDPmf' * kron(iF,iF) * vecDPmf);
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% for ii = 1:k
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%
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% diFi = -iF*DF(:,:,ii)*iF;
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% for jj = 1:ii
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% dFj = DF(:,:,jj);
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% diFj = -iF*DF(:,:,jj)*iF;
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%
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% Hesst(ii,jj) = getHesst_ij(v*0,Dv(:,ii),Dv(:,jj),v*0,iF,diFi,diFj,0,-dFj,0);
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% end
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% end
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end
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% end of computeDLIK
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function Hesst_ij = getHesst_ij(e,dei,dej,d2eij,iS,diSi,diSj,d2iSij,dSj,d2Sij);
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@ -1,4 +1,4 @@
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function [LIK, likk, a, P] = kalman_filter(Y,start,last,a,P,kalman_tol,riccati_tol,presample,T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods,analytic_derivation,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P)
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function [LIK, LIKK, a, P] = kalman_filter(Y,start,last,a,P,kalman_tol,riccati_tol,presample,T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods,analytic_derivation,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P)
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% Computes the likelihood of a stationnary state space model.
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%@info:
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@ -123,6 +123,7 @@ LIK = Inf; % Default value of the log likelihood.
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oldK = Inf;
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notsteady = 1;
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F_singular = 1;
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asy_hess=0;
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if analytic_derivation == 0,
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DLIK=[];
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@ -131,6 +132,7 @@ else
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k = size(DT,3); % number of structural parameters
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DLIK = zeros(k,1); % Initialization of the score.
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Da = zeros(mm,k); % Derivative State vector.
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dlikk = zeros(smpl,k);
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if Zflag==0,
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C = zeros(pp,mm);
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@ -144,12 +146,17 @@ else
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D2a = zeros(mm,k,k); % State vector.
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d2C = zeros(pp,mm,k,k);
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else
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asy_hess=D2T;
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Hess=[];
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D2a=[];
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D2T=[];
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D2Yss=[];
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end
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if asy_hess,
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Hess = zeros(k,k); % Initialization of the Hessian
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end
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LIK={inf,DLIK,Hess};
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LIKK={likk,dlikk};
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end
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while notsteady && t<=last
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@ -185,14 +192,15 @@ while notsteady && t<=last
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if analytic_derivation==2,
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[Da,DP,DLIKt,D2a,D2P, Hesst] = computeDLIK(k,tmp,Z,Zflag,v,T,K,P,iF,Da,DYss,DT,DOm,DP,DH,notsteady,D2a,D2Yss,D2T,D2Om,D2P);
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else
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[Da,DP,DLIKt] = computeDLIK(k,tmp,Z,Zflag,v,T,K,P,iF,Da,DYss,DT,DOm,DP,DH,notsteady);
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[Da,DP,DLIKt,Hesst] = computeDLIK(k,tmp,Z,Zflag,v,T,K,P,iF,Da,DYss,DT,DOm,DP,DH,notsteady);
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end
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if t>presample
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DLIK = DLIK + DLIKt;
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if analytic_derivation==2,
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if analytic_derivation==2 || asy_hess,
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Hess = Hess + Hesst;
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end
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end
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dlikk(s,:)=DLIKt;
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end
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a = T*tmp;
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P=Ptmp;
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@ -208,19 +216,31 @@ end
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% Add observation's densities constants and divide by two.
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likk(1:s) = .5*(likk(1:s) + pp*log(2*pi));
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if analytic_derivation,
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DLIK = DLIK/2;
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dlikk = dlikk/2;
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if analytic_derivation==2 || asy_hess,
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if asy_hess==0,
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Hess = Hess + tril(Hess,-1)';
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end
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Hess = -Hess/2;
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end
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end
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% Call steady state Kalman filter if needed.
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if t <= last
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if analytic_derivation,
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if analytic_derivation==2,
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[tmp, likk(s+1:end)] = kalman_filter_ss(Y,t,last,a,T,K,iF,dF,Z,pp,Zflag, ...
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[tmp, tmp2] = kalman_filter_ss(Y,t,last,a,T,K,iF,dF,Z,pp,Zflag, ...
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analytic_derivation,Da,DT,DYss,D2a,D2T,D2Yss);
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else
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[tmp, likk(s+1:end)] = kalman_filter_ss(Y,t,last,a,T,K,iF,dF,Z,pp,Zflag, ...
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analytic_derivation,Da,DT,DYss);
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[tmp, tmp2] = kalman_filter_ss(Y,t,last,a,T,K,iF,dF,Z,pp,Zflag, ...
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analytic_derivation,Da,DT,DYss,asy_hess);
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end
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likk(s+1:end)=tmp2{1};
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dlikk(s+1:end,:)=tmp2{2};
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DLIK = DLIK + tmp{2};
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if analytic_derivation==2,
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if analytic_derivation==2 || asy_hess,
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Hess = Hess + tmp{3};
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end
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else
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@ -229,20 +249,19 @@ if t <= last
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end
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% Compute minus the log-likelihood.
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if presample
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if presample>=diffuse_periods
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likk = likk(1+(presample-diffuse_periods):end);
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end
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if presample>diffuse_periods,
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LIK = sum(likk(1+(presample-diffuse_periods):end));
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else
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LIK = sum(likk);
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end
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LIK = sum(likk);
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if analytic_derivation,
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DLIK = DLIK/2;
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if analytic_derivation==2,
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Hess = Hess + tril(Hess,-1)';
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Hess = -Hess/2;
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if analytic_derivation==2 || asy_hess,
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LIK={LIK, DLIK, Hess};
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else
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LIK={LIK, DLIK};
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end
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LIKK={likk, dlikk};
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else
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LIKK=likk;
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end
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@ -81,6 +81,7 @@ t = start; % Initialization of the time index.
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likk = zeros(smpl,1); % Initialization of the vector gathering the densities.
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LIK = Inf; % Default value of the log likelihood.
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notsteady = 0;
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asy_hess=0;
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if nargin<12
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analytic_derivation = 0;
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end
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@ -91,10 +92,16 @@ if analytic_derivation == 0,
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else
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k = size(DT,3); % number of structural parameters
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DLIK = zeros(k,1); % Initialization of the score.
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dlikk = zeros(smpl,k);
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if analytic_derivation==2,
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Hess = zeros(k,k); % Initialization of the Hessian
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else
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Hess=[];
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asy_hess=D2a;
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if asy_hess,
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Hess = zeros(k,k); % Initialization of the Hessian
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else
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Hess=[];
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end
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end
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end
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@ -109,12 +116,13 @@ while t <= last
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if analytic_derivation==2,
|
||||
[Da,junk,DLIKt,D2a,junk2, Hesst] = computeDLIK(k,tmp,Z,Zflag,v,T,K,[],iF,Da,DYss,DT,[],[],[],notsteady,D2a,D2Yss,D2T,[],[]);
|
||||
else
|
||||
[Da,junk,DLIKt] = computeDLIK(k,tmp,Z,Zflag,v,T,K,[],iF,Da,DYss,DT,[],[],[],notsteady);
|
||||
[Da,junk,DLIKt,Hesst] = computeDLIK(k,tmp,Z,Zflag,v,T,K,[],iF,Da,DYss,DT,[],[],[],notsteady);
|
||||
end
|
||||
DLIK = DLIK + DLIKt;
|
||||
if analytic_derivation==2,
|
||||
if analytic_derivation==2 || asy_hess,
|
||||
Hess = Hess + Hesst;
|
||||
end
|
||||
dlikk(t-start+1,:)=DLIKt;
|
||||
end
|
||||
a = T*tmp;
|
||||
likk(t-start+1) = transpose(v)*iF*v;
|
||||
|
|
@ -129,9 +137,17 @@ likk = .5*(likk + pp*log(2*pi));
|
|||
|
||||
% Sum the observation's densities (minus the likelihood)
|
||||
LIK = sum(likk);
|
||||
if analytic_derivation==2,
|
||||
LIK={LIK,DLIK,Hess};
|
||||
if analytic_derivation,
|
||||
dlikk = dlikk/2;
|
||||
DLIK = DLIK/2;
|
||||
likk = {likk, dlikk};
|
||||
end
|
||||
if analytic_derivation==1,
|
||||
if analytic_derivation==2 || asy_hess,
|
||||
if asy_hess==0,
|
||||
Hess = Hess + tril(Hess,-1)';
|
||||
end
|
||||
Hess = -Hess/2;
|
||||
LIK={LIK,DLIK,Hess};
|
||||
elseif analytic_derivation==1,
|
||||
LIK={LIK,DLIK};
|
||||
end
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ if notsteady,
|
|||
DF(j)=Z*DP(:,:,j)*Z'+DH;
|
||||
DK(:,j) = (DP(:,:,j)*Z')/F-PZ*DF(j)/F^2;
|
||||
end
|
||||
if nargout>3
|
||||
if nargout>4
|
||||
D2F = zeros(k,k);
|
||||
D2v = zeros(k,k);
|
||||
D2K = zeros(rows(K),k,k);
|
||||
|
|
@ -50,7 +50,7 @@ if notsteady,
|
|||
Dv = -Da(Z,:) - DYss(Z,:);
|
||||
DF = squeeze(DP(Z,Z,:))+DH';
|
||||
DK = squeeze(DP(:,Z,:))/F-PZ*transpose(DF)/F^2;
|
||||
if nargout>3
|
||||
if nargout>4
|
||||
D2v = squeeze(-D2a(Z,:,:) - D2Yss(Z,:,:));
|
||||
D2F = squeeze(D2P(Z,Z,:,:));
|
||||
D2K = squeeze(D2P(:,Z,:,:))/F;
|
||||
|
|
@ -60,7 +60,7 @@ if notsteady,
|
|||
end
|
||||
end
|
||||
end
|
||||
if nargout>3
|
||||
if nargout>4
|
||||
DD2K(:,indx,:,:)=D2K;
|
||||
DD2F(indx,:,:)=D2F;
|
||||
end
|
||||
|
|
@ -69,13 +69,13 @@ if notsteady,
|
|||
else
|
||||
DK = squeeze(DDK(:,indx,:));
|
||||
DF = DDF(indx,:)';
|
||||
if nargout>3
|
||||
if nargout>4
|
||||
D2K = squeeze(DD2K(:,indx,:,:));
|
||||
D2F = squeeze(DD2F(indx,:,:));
|
||||
end
|
||||
if Zflag
|
||||
Dv = -Z*Da(:,:) - Z*DYss(:,:);
|
||||
if nargout>3
|
||||
if nargout>4
|
||||
D2v = zeros(k,k);
|
||||
for j=1:k,
|
||||
D2v(:,j) = -Z*D2a(:,:,j) - Z*D2Yss(:,:,j);
|
||||
|
|
@ -83,7 +83,7 @@ else
|
|||
end
|
||||
else
|
||||
Dv = -Da(Z,:) - DYss(Z,:);
|
||||
if nargout>3
|
||||
if nargout>4
|
||||
D2v = squeeze(-D2a(Z,:,:) - D2Yss(Z,:,:));
|
||||
end
|
||||
end
|
||||
|
|
@ -93,10 +93,15 @@ DLIK = DF/F + 2*Dv'/F*v - v^2/F^2*DF;
|
|||
if nargout==6
|
||||
Hesst = D2F/F-1/F^2*(DF*DF') + 2*D2v/F*v + 2*(Dv'*Dv)/F - 2*(DF*Dv)*v/F^2 ...
|
||||
- v^2/F^2*D2F - 2*v/F^2*(Dv'*DF') + 2*v^2/F^3*(DF*DF');
|
||||
elseif nargout==4,
|
||||
D2a = 1/F^2*(DF*DF') + 2*(Dv'*Dv)/F ;
|
||||
% D2a = -1/F^2*(DF*DF') + 2*(Dv'*Dv)/F + 2*v^2/F^3*(DF*DF') ...
|
||||
% - 2*(DF*Dv)*v/F^2 - 2*v/F^2*(Dv'*DF');
|
||||
% D2a = +2*(Dv'*Dv)/F + (DF' * DF)/F^2;
|
||||
end
|
||||
|
||||
Da = Da + DK*v+K*Dv;
|
||||
if nargout>3
|
||||
if nargout>4
|
||||
|
||||
D2a = D2a + D2K*v;
|
||||
for j=1:k,
|
||||
|
|
@ -121,7 +126,7 @@ if notsteady,
|
|||
DP1(:,:,j)=DP(:,:,j) - (DP(:,Z,j))*K'-PZ*DK(:,j)';
|
||||
end
|
||||
end
|
||||
if nargout>3,
|
||||
if nargout>4,
|
||||
if Zflag,
|
||||
for j=1:k,
|
||||
D2P = D2P;
|
||||
|
|
|
|||
|
|
@ -119,6 +119,7 @@ notsteady = 1;
|
|||
|
||||
oldK = Inf;
|
||||
K = NaN(mm,pp);
|
||||
asy_hess=0;
|
||||
|
||||
if analytic_derivation == 0,
|
||||
DLIK=[];
|
||||
|
|
@ -127,6 +128,7 @@ else
|
|||
k = size(DT,3); % number of structural parameters
|
||||
DLIK = zeros(k,1); % Initialization of the score.
|
||||
Da = zeros(mm,k); % Derivative State vector.
|
||||
dlik = zeros(smpl,k);
|
||||
|
||||
if Zflag==0,
|
||||
C = zeros(pp,mm);
|
||||
|
|
@ -140,11 +142,15 @@ else
|
|||
D2a = zeros(mm,k,k); % State vector.
|
||||
d2C = zeros(pp,mm,k,k);
|
||||
else
|
||||
asy_hess=D2T;
|
||||
Hess=[];
|
||||
D2a=[];
|
||||
D2T=[];
|
||||
D2Yss=[];
|
||||
end
|
||||
if asy_hess,
|
||||
Hess = zeros(k,k); % Initialization of the Hessian
|
||||
end
|
||||
LIK={inf,DLIK,Hess};
|
||||
end
|
||||
|
||||
|
|
@ -177,14 +183,15 @@ while notsteady && t<=last
|
|||
if analytic_derivation==2,
|
||||
[Da,DP,DLIKt,D2a,D2P, Hesst] = univariate_computeDLIK(k,i,z(i,:),Zflag,prediction_error,Ki,PZ,Fi,Da,DYss,DP,DH(d_index(i),:),notsteady,D2a,D2Yss,D2P);
|
||||
else
|
||||
[Da,DP,DLIKt] = univariate_computeDLIK(k,i,z(i,:),Zflag,prediction_error,Ki,PZ,Fi,Da,DYss,DP,DH(d_index(i),:),notsteady);
|
||||
[Da,DP,DLIKt,Hesst] = univariate_computeDLIK(k,i,z(i,:),Zflag,prediction_error,Ki,PZ,Fi,Da,DYss,DP,DH(d_index(i),:),notsteady);
|
||||
end
|
||||
if t>presample
|
||||
DLIK = DLIK + DLIKt;
|
||||
if analytic_derivation==2,
|
||||
if analytic_derivation==2 || asy_hess,
|
||||
Hess = Hess + Hesst;
|
||||
end
|
||||
end
|
||||
dlik(s,:)=DLIKt;
|
||||
end
|
||||
a = a + Ki*prediction_error;
|
||||
P = P - PZ*Ki';
|
||||
|
|
@ -208,19 +215,29 @@ end
|
|||
|
||||
% Divide by two.
|
||||
lik(1:s) = .5*lik(1:s);
|
||||
if analytic_derivation,
|
||||
DLIK = DLIK/2;
|
||||
dlik = dlik/2;
|
||||
if analytic_derivation==2 || asy_hess,
|
||||
% Hess = (Hess + Hess')/2;
|
||||
Hess = -Hess/2;
|
||||
end
|
||||
end
|
||||
|
||||
% Call steady state univariate kalman filter if needed.
|
||||
if t <= last
|
||||
if analytic_derivation,
|
||||
if analytic_derivation==2,
|
||||
[tmp, lik(s+1:end)] = univariate_kalman_filter_ss(Y,t,last,a,P,kalman_tol,T,H,Z,pp,Zflag, ...
|
||||
[tmp, tmp2] = univariate_kalman_filter_ss(Y,t,last,a,P,kalman_tol,T,H,Z,pp,Zflag, ...
|
||||
analytic_derivation,Da,DT,DYss,DP,DH,D2a,D2T,D2Yss,D2P);
|
||||
else
|
||||
[tmp, lik(s+1:end)] = univariate_kalman_filter_ss(Y,t,last,a,P,kalman_tol,T,H,Z,pp,Zflag, ...
|
||||
analytic_derivation,Da,DT,DYss,DP,DH);
|
||||
[tmp, tmp2] = univariate_kalman_filter_ss(Y,t,last,a,P,kalman_tol,T,H,Z,pp,Zflag, ...
|
||||
analytic_derivation,Da,DT,DYss,DP,DH,asy_hess);
|
||||
end
|
||||
lik(s+1:end)=tmp2{1};
|
||||
dlik(s+1:end,:)=tmp2{2};
|
||||
DLIK = DLIK + tmp{2};
|
||||
if analytic_derivation==2,
|
||||
if analytic_derivation==2 || asy_hess,
|
||||
Hess = Hess + tmp{3};
|
||||
end
|
||||
else
|
||||
|
|
@ -236,11 +253,10 @@ else
|
|||
end
|
||||
|
||||
if analytic_derivation,
|
||||
DLIK = DLIK/2;
|
||||
if analytic_derivation==2,
|
||||
Hess = -Hess/2;
|
||||
if analytic_derivation==2 || asy_hess,
|
||||
LIK={LIK, DLIK, Hess};
|
||||
else
|
||||
LIK={LIK, DLIK};
|
||||
end
|
||||
lik={lik, dlik};
|
||||
end
|
||||
|
|
|
|||
|
|
@ -82,6 +82,7 @@ t = start; % Initialization of the time index.
|
|||
likk = zeros(smpl,1); % Initialization of the vector gathering the densities.
|
||||
LIK = Inf; % Default value of the log likelihood.
|
||||
l2pi = log(2*pi);
|
||||
asy_hess=0;
|
||||
|
||||
if nargin<12
|
||||
analytic_derivation = 0;
|
||||
|
|
@ -93,10 +94,16 @@ if analytic_derivation == 0,
|
|||
else
|
||||
k = size(DT,3); % number of structural parameters
|
||||
DLIK = zeros(k,1); % Initialization of the score.
|
||||
dlikk = zeros(smpl,k);
|
||||
if analytic_derivation==2,
|
||||
Hess = zeros(k,k); % Initialization of the Hessian
|
||||
else
|
||||
Hess=[];
|
||||
asy_hess=D2a;
|
||||
if asy_hess,
|
||||
Hess = zeros(k,k); % Initialization of the Hessian
|
||||
else
|
||||
Hess=[];
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
|
|
@ -129,12 +136,13 @@ while t<=last
|
|||
if analytic_derivation==2,
|
||||
[Da,DPP,DLIKt,D2a,D2PP, Hesst] = univariate_computeDLIK(k,i,Z(i,:),Zflag,prediction_error,Ki,PPZ,Fi,Da,DYss,DPP,DH(i,:),0,D2a,D2Yss,D2PP);
|
||||
else
|
||||
[Da,DPP,DLIKt] = univariate_computeDLIK(k,i,Z(i,:),Zflag,prediction_error,Ki,PPZ,Fi,Da,DYss,DPP,DH(i,:),0);
|
||||
[Da,DPP,DLIKt,Hesst] = univariate_computeDLIK(k,i,Z(i,:),Zflag,prediction_error,Ki,PPZ,Fi,Da,DYss,DPP,DH(i,:),0);
|
||||
end
|
||||
DLIK = DLIK + DLIKt;
|
||||
if analytic_derivation==2,
|
||||
if analytic_derivation==2 || asy_hess,
|
||||
Hess = Hess + Hesst;
|
||||
end
|
||||
dlikk(s,:)=DLIKt;
|
||||
end
|
||||
end
|
||||
end
|
||||
|
|
@ -152,9 +160,15 @@ end
|
|||
likk = .5*likk;
|
||||
|
||||
LIK = sum(likk);
|
||||
if analytic_derivation==2,
|
||||
LIK={LIK,DLIK,Hess};
|
||||
if analytic_derivation,
|
||||
dlikk = dlikk/2;
|
||||
DLIK = DLIK/2;
|
||||
likk = {likk, dlikk};
|
||||
end
|
||||
if analytic_derivation==1,
|
||||
if analytic_derivation==2 || asy_hess,
|
||||
% Hess = (Hess + Hess')/2;
|
||||
Hess = -Hess/2;
|
||||
LIK={LIK,DLIK,Hess};
|
||||
elseif analytic_derivation==1,
|
||||
LIK={LIK,DLIK};
|
||||
end
|
||||
|
|
@ -21,6 +21,10 @@ function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,DynareDataset,DynareOptions,Mod
|
|||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
n=size(x,1);
|
||||
if isempty(h1),
|
||||
h1=DynareOptions.gradient_epsilon*ones(n,1);
|
||||
end
|
||||
|
||||
|
||||
if isempty(htol0)
|
||||
htol = 1.e-6;
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function [xparam1, hh, gg, fval, igg] = newrat(func0, x, hh, gg, igg, ftol0, nit, flagg, DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
|
||||
function [xparam1, hh, gg, fval, igg] = newrat(func0, x, analytic_derivation, ftol0, nit, flagg, DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
|
||||
% [xparam1, hh, gg, fval, igg] = newrat(func0, x, hh, gg, igg, ftol0, nit, flagg, varargin)
|
||||
%
|
||||
% Optimiser with outer product gradient and with sequences of univariate steps
|
||||
|
|
@ -10,9 +10,7 @@ function [xparam1, hh, gg, fval, igg] = newrat(func0, x, hh, gg, igg, ftol0, nit
|
|||
% of the log-likelihood to compute outer product gradient
|
||||
%
|
||||
% x = starting guess
|
||||
% hh = initial Hessian [OPTIONAL]
|
||||
% gg = initial gradient [OPTIONAL]
|
||||
% igg = initial inverse Hessian [OPTIONAL]
|
||||
% analytic_derivation = 1 if analytic derivs
|
||||
% ftol0 = ending criterion for function change
|
||||
% nit = maximum number of iterations
|
||||
%
|
||||
|
|
@ -56,14 +54,14 @@ gibbstol=length(BayesInfo.pshape)/50; %25;
|
|||
|
||||
% func0 = str2func([func2str(func0),'_hh']);
|
||||
% func0 = func0;
|
||||
fval0=feval(func0,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
[fval0,gg,hh]=feval(func0,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
fval=fval0;
|
||||
|
||||
% initialize mr_gstep and mr_hessian
|
||||
mr_hessian(1,x,[],[],[],DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
outer_product_gradient=1;
|
||||
|
||||
outer_product_gradient=1;
|
||||
if isempty(hh)
|
||||
mr_hessian(1,x,[],[],[],DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
[dum, gg, htol0, igg, hhg, h1]=mr_hessian(0,x,func0,flagit,htol,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
if isempty(dum),
|
||||
outer_product_gradient=0;
|
||||
|
|
@ -85,6 +83,7 @@ else
|
|||
hh0=hh;
|
||||
hhg=hh;
|
||||
igg=inv(hh);
|
||||
h1=[];
|
||||
end
|
||||
H = igg;
|
||||
disp(['Gradient norm ',num2str(norm(gg))])
|
||||
|
|
@ -125,7 +124,11 @@ while norm(gg)>gtol && check==0 && jit<nit
|
|||
if length(find(ig))<nx
|
||||
ggx=ggx*0;
|
||||
ggx(find(ig))=gg(find(ig));
|
||||
hhx = reshape(dum,nx,nx);
|
||||
if analytic_derivation,
|
||||
hhx=hh;
|
||||
else
|
||||
hhx = reshape(dum,nx,nx);
|
||||
end
|
||||
iggx=eye(length(gg));
|
||||
iggx(find(ig),find(ig)) = inv( hhx(find(ig),find(ig)) );
|
||||
[fvala,x0,fc,retcode] = csminit1(func0,x0,fval,ggx,0,iggx,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
|
|
@ -159,6 +162,11 @@ while norm(gg)>gtol && check==0 && jit<nit
|
|||
if (fval0(icount)-fval)<ftol
|
||||
disp('No further improvement is possible!')
|
||||
check=1;
|
||||
if analytic_derivation,
|
||||
[fvalx,gg,hh]=feval(func0,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
hhg=hh;
|
||||
H = inv(hh);
|
||||
else
|
||||
if flagit==2
|
||||
hh=hh0;
|
||||
elseif flagg>0
|
||||
|
|
@ -173,6 +181,7 @@ while norm(gg)>gtol && check==0 && jit<nit
|
|||
hh=hhg;
|
||||
end
|
||||
end
|
||||
end
|
||||
disp(['Actual dxnorm ',num2str(norm(x(:,end)-x(:,end-1)))])
|
||||
disp(['FVAL ',num2str(fval)])
|
||||
disp(['Improvement ',num2str(fval0(icount)-fval)])
|
||||
|
|
@ -191,7 +200,7 @@ while norm(gg)>gtol && check==0 && jit<nit
|
|||
disp(['Ftol ',num2str(ftol)])
|
||||
disp(['Htol ',num2str(htol0)])
|
||||
htol=htol_base;
|
||||
if norm(x(:,icount)-xparam1)>1.e-12
|
||||
if norm(x(:,icount)-xparam1)>1.e-12 && analytic_derivation==0,
|
||||
try
|
||||
save m1.mat x fval0 nig -append
|
||||
catch
|
||||
|
|
@ -225,6 +234,10 @@ while norm(gg)>gtol && check==0 && jit<nit
|
|||
end
|
||||
H = igg;
|
||||
end
|
||||
else
|
||||
[fvalx,gg,hh]=feval(func0,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
hhg=hh;
|
||||
H = inv(hh);
|
||||
end
|
||||
disp(['Gradient norm ',num2str(norm(gg))])
|
||||
ee=eig(hh);
|
||||
|
|
|
|||
Loading…
Reference in New Issue