removing unused function DsgeLikelihood_hh.m
git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1915 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
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function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)
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% function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)
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% Evaluates the likelihood at each observation and the marginal density of a dsge model
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% used in the optimization algorithm number 5
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%
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% INPUTS
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% xparam1: vector of model parameters
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% gend : scalar specifying the number of observations
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% data : matrix of data
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%
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% OUTPUTS
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% fval : value of the posterior kernel at xparam1
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% llik : gives the density at each observation
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% cost_flag : zero if the function returns a penalty, one otherwise
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% ys : steady state of original endogenous variables
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% trend_coeff :
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% info : vector of informations about the penalty
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%
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% SPECIAL REQUIREMENTS
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% Adapted from dsgelikelihood.m
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%
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% copyright marco.ratto@jrc.it [13-03-2007]
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global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_ xparam1_test
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fval = [];
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ys = [];
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trend_coeff = [];
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xparam1_test = xparam1;
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cost_flag = 1;
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nobs = size(options_.varobs,1);
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%------------------------------------------------------------------------------
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% 1. Get the structural parameters & define penalties
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%------------------------------------------------------------------------------
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if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
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k = find(xparam1 < bayestopt_.lb);
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fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
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llik=fval;
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cost_flag = 0;
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info = 41;
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return;
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end
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if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
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k = find(xparam1 > bayestopt_.ub);
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fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
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llik=fval;
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cost_flag = 0;
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info = 42;
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return;
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end
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Q = M_.Sigma_e;
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H = M_.H;
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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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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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offset = offset+estim_params_.nvn;
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end
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if estim_params_.ncx
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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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[CholQ,testQ] = chol(Q);
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if testQ %% The variance-covariance matrix of the structural innovations is not definite positive.
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%% We have to compute the eigenvalues of this matrix in order to build the penalty.
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a = diag(eig(Q));
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k = find(a < 0);
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if k > 0
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fval = bayestopt_.penalty+sum(-a(k));
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llik=fval;
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cost_flag = 0;
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info = 43;
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return
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end
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end
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offset = offset+estim_params_.ncx;
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end
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if 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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[CholH,testH] = chol(H);
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if testH
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a = diag(eig(H));
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k = find(a < 0);
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if k > 0
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fval = bayestopt_.penalty+sum(-a(k));
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llik=fval;
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cost_flag = 0;
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info = 44;
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return
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end
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end
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offset = offset+estim_params_.ncn;
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end
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M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1: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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M_.H = H;
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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,info] = dynare_resolve(bayestopt_.restrict_var_list,...
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bayestopt_.restrict_columns,...
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bayestopt_.restrict_aux);
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if info(1) == 1 | info(1) == 2 | info(1) == 5
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fval = bayestopt_.penalty+1;
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llik=fval;
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cost_flag = 0;
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return
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elseif info(1) == 3 | info(1) == 4 | info(1) == 20
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fval = bayestopt_.penalty+info(2)^2;
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llik=fval;
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cost_flag = 0;
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return
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end
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bayestopt_.mf = bayestopt_.mf1;
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if ~options_.noconstant
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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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else
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constant = zeros(nobs,1);
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end
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if bayestopt_.with_trend == 1
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trend_coeff = zeros(nobs,1);
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t = options_.trend_coeffs;
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for i=1:length(t)
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if ~isempty(t{i})
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trend_coeff(i) = evalin('base',t{i});
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end
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end
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trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
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else
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trend = repmat(constant,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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if options_.lik_init == 1 % Kalman filter
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Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium);
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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_.restrict_var_list_stationary;
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ivd = bayestopt_.restrict_var_list_nonstationary;
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RR=T(:,bayestopt_.restrict_var_list_nonstationary);
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i=find(abs(RR)>1.e-10);
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R0=zeros(size(RR));
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R0(i)=sign(RR(i));
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Pinf=R0*R0';
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T0 = T;
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R1 = R;
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for j=1:size(T,1),
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for i=1:length(ivd),
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T0(j,:) = T0(j,:)-RR(j,i).*T(ivd(i),:);
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R1(j,:) = R1(j,:)-RR(j,i).*R(ivd(i),:);
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end
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end
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Pstar = lyapunov_symm(T0,R1*Q*R1',options_.qz_criterium);
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end
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%------------------------------------------------------------------------------
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% 4. Likelihood evaluation
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%------------------------------------------------------------------------------
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if any(any(H ~= 0)) % should be replaced by a flag
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if options_.kalman_algo == 1
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[LIK, lik] =DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,data,trend,start);
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if isinf(LIK) & ~estim_params_.ncn %% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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[LIK, lik] =DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
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elseif isinf(LIK) & estim_params_.ncn
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[LIK, lik] =DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
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end
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elseif options_.kalman_algo == 3
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if ~estim_params_.ncn %% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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[LIK, lik] =DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
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else
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[LIK, lik] =DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
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end
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end
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else
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if options_.kalman_algo == 1
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%nv = size(bayestopt_.Z,1);
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%LIK = kalman_filter(bayestopt_.Z,zeros(nv,nv),T,R,Q,data,zeros(size(T,1),1),Pstar,'u');
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[LIK, lik] =DiffuseLikelihood1(T,R,Q,Pinf,Pstar,data,trend,start);
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% LIK = diffuse_likelihood1(T,R,Q,Pinf,Pstar,data-trend,start);
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%if abs(LIK1-LIK)>0.0000000001
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% disp(['LIK1 and LIK are not equal! ' num2str(abs(LIK1-LIK))])
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%end
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if isinf(LIK)
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[LIK, lik] =DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
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end
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elseif options_.kalman_algo == 3
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[LIK, lik] =DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
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end
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end
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if imag(LIK) ~= 0
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likelihood = bayestopt_.penalty;
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lik=ones(size(lik)).*bayestopt_.penalty;
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else
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likelihood = LIK;
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end
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% ------------------------------------------------------------------------------
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% Adds prior if necessary
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% ------------------------------------------------------------------------------
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lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p1,bayestopt_.p2,bayestopt_.p3,bayestopt_.p4);
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fval = (likelihood-lnprior);
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llik=[-lnprior; .5*lik(start:end)];
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