Initial particles are drawn in the prior distribution + bug fixes.
Frédéric Karamé
parent
d9d19332d1
commit
d549e26a40
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@ -43,7 +43,7 @@ persistent start_param sample_size number_of_observed_variables number_of_struct
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% Set seed for randn().
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set_dynare_seed('default') ;
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pruning = DynareOptions.particle.pruning;
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second_resample = 0 ;
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second_resample = DynareOptions.particle.resampling.status.systematic ;
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variance_update = 1 ;
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% initialization of state particles
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@ -75,25 +75,31 @@ if pruning
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end
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% parameters for the Liu & West filter
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h_square = (3*liu_west_delta-1)/(2*liu_west_delta) ;
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h_square = 1-h_square*h_square ;
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small_a = sqrt(1-h_square) ;
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small_a = (3*liu_west_delta-1)/(2*liu_west_delta) ;
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b_square = 1-small_a*small_a ;
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% Initialization of parameter particles
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xparam = zeros(number_of_parameters,number_of_particles) ;
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stderr = sqrt(bsxfun(@power,bounds.ub-bounds.lb,2)/12)/100 ;
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stderr = sqrt(bsxfun(@power,bounds.ub-bounds.lb,2)/12)/50 ;
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%stderr = sqrt(bsxfun(@power,bounds.ub-bounds.lb,2)/12)/100 ;
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%stderr = sqrt(bsxfun(@power,bounds.ub-bounds.lb,2)/12)/50 ;
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%stderr = sqrt(bsxfun(@power,bounds.ub-bounds.lb,2)/12)/20 ;
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i = 1 ;
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while i<=number_of_particles
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%candidate = start_param + .001*liu_west_chol_sigma_bar*randn(number_of_parameters,1) ;
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candidate = start_param + bsxfun(@times,stderr,randn(number_of_parameters,1)) ;
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if all(candidate(:) >= bounds.lb) && all(candidate(:) <= bounds.ub)
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xparam(:,i) = candidate(:) ;
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i = i+1 ;
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bounds = prior_bounds(BayesInfo,DynareOptions.prior_trunc); %reset bounds as lb and ub must only be operational during mode-finding
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prior_draw(BayesInfo,DynareOptions.prior_trunc);
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for i=1:number_of_particles
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info = 1;
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while info==1
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%candidate = start_param + .001*liu_west_chol_sigma_bar*randn(number_of_parameters,1) ;
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%candidate = start_param + bsxfun(@times,stderr,randn(number_of_parameters,1)) ;
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candidate = prior_draw()';
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if all(candidate(:) >= bounds.lb) && all(candidate(:) <= bounds.ub)
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[ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
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solve_model_for_online_filter(1,candidate(:),DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults) ;
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if info==0
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xparam(:,i) = candidate(:) ;
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end
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end
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end
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end
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end
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%xparam = bsxfun(@plus,bounds(:,1),bsxfun(@times,(bounds(:,2)-bounds(:,1)),rand(number_of_parameters,number_of_particles))) ;
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% Initialization of the weights of particles.
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@ -119,81 +125,87 @@ for t=1:sample_size
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temp = bsxfun(@minus,xparam,m_bar) ;
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sigma_bar = (bsxfun(@times,weights,temp))*(temp') ;
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if variance_update==1
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chol_sigma_bar = chol(h_square*sigma_bar)' ;
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chol_sigma_bar = chol(b_square*sigma_bar)' ;
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end
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% Prediction (without shocks)
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fore_xparam = bsxfun(@plus,(1-small_a).*m_bar,small_a.*xparam) ;
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tau_tilde = zeros(1,number_of_particles) ;
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for i=1:number_of_particles
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% model resolution
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[ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
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solve_model_for_online_filter(t,xparam(:,i),DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults) ;
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steadystate = ReducedForm.steadystate;
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state_variables_steady_state = ReducedForm.state_variables_steady_state;
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% Set local state space model (second-order approximation).
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constant = ReducedForm.constant;
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ghx = ReducedForm.ghx;
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ghu = ReducedForm.ghu;
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ghxx = ReducedForm.ghxx;
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ghuu = ReducedForm.ghuu;
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ghxu = ReducedForm.ghxu;
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% particle likelihood contribution
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yhat = bsxfun(@minus,StateVectors(:,i),state_variables_steady_state);
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if pruning
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yhat_ = bsxfun(@minus,StateVectors_(:,i),state_variables_steady_state);
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[tmp, tmp_] = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,1),ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,DynareOptions.threads.local_state_space_iteration_2);
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else
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tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,1),ghx,ghu,constant,ghxx,ghuu,ghxu,DynareOptions.threads.local_state_space_iteration_2);
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end
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PredictionError = bsxfun(@minus,Y(t,:)',tmp(mf1,:));
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% Replace Gaussian density with a Student density with 3 degrees of
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% freedom for fat tails.
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z = sum(PredictionError.*(ReducedForm.H\PredictionError),1) ;
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tau_tilde(i) = weights(i).*(tpdf(z,3*ones(size(z)))+1e-99) ;
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%tau_tilde(i) = weights(i).*exp(-.5*(const_lik+log(det(ReducedForm.H))+sum(PredictionError.*(ReducedForm.H\PredictionError),1))) ;
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end
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% particles selection
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tau_tilde = tau_tilde/sum(tau_tilde) ;
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indx = resample(0,tau_tilde',DynareOptions.particle);
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StateVectors = StateVectors(:,indx) ;
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if pruning
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StateVectors_ = StateVectors_(:,indx) ;
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end
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xparam = bsxfun(@plus,(1-small_a).*m_bar,small_a.*xparam(:,indx)) ;
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w_stage1 = weights(indx)./tau_tilde(indx) ;
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% draw in the new distributions
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wtilde = zeros(1,number_of_particles) ;
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i = 1 ;
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while i<=number_of_particles
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candidate = xparam(:,i) + chol_sigma_bar*randn(number_of_parameters,1) ;
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if all(candidate >= bounds.lb) && all(candidate <= bounds.ub)
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xparam(:,i) = candidate ;
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% model resolution for new parameters particles
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[ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
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solve_model_for_online_filter(t,xparam(:,i),DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults) ;
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solve_model_for_online_filter(t,fore_xparam(:,i),DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults) ;
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if info==0
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steadystate = ReducedForm.steadystate;
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state_variables_steady_state = ReducedForm.state_variables_steady_state;
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% Set local state space model (second order approximation).
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% Set local state space model (second-order approximation).
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constant = ReducedForm.constant;
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ghx = ReducedForm.ghx;
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ghu = ReducedForm.ghu;
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ghxx = ReducedForm.ghxx;
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ghuu = ReducedForm.ghuu;
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ghxu = ReducedForm.ghxu;
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% Get covariance matrices and structural shocks
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epsilon = chol(ReducedForm.Q)'*randn(number_of_structural_innovations,1) ;
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% compute particles likelihood contribution
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% particle likelihood contribution
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yhat = bsxfun(@minus,StateVectors(:,i),state_variables_steady_state);
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if pruning
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yhat_ = bsxfun(@minus,StateVectors_(:,i),state_variables_steady_state);
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[tmp, tmp_] = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,DynareOptions.threads.local_state_space_iteration_2);
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StateVectors_(:,i) = tmp_(mf0,:) ;
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[tmp, tmp_] = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,1),ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,DynareOptions.threads.local_state_space_iteration_2);
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else
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tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,DynareOptions.threads.local_state_space_iteration_2);
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tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,1),ghx,ghu,constant,ghxx,ghuu,ghxu,DynareOptions.threads.local_state_space_iteration_2);
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end
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StateVectors(:,i) = tmp(mf0,:) ;
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PredictionError = bsxfun(@minus,Y(t,:)',tmp(mf1,:));
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wtilde(i) = w_stage1(i)*exp(-.5*(const_lik+log(det(ReducedForm.H))+sum(PredictionError.*(ReducedForm.H\PredictionError),1)));
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i = i+1 ;
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% Replace Gaussian density with a Student density with 3 degrees of
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% freedom for fat tails.
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z = sum(PredictionError.*(ReducedForm.H\PredictionError),1) ;
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tau_tilde(i) = weights(i).*(tpdf(z,3*ones(size(z)))+1e-99) ;
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%tau_tilde(i) = weights(i).*exp(-.5*(const_lik+log(det(ReducedForm.H))+sum(PredictionError.*(ReducedForm.H\PredictionError),1))) ;
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end
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end
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% particles selection
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tau_tilde = tau_tilde/sum(tau_tilde) ;
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indx = resample(0,tau_tilde',DynareOptions.particle);
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StateVectors = StateVectors(:,indx) ;
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xparam = fore_xparam(:,indx);
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if pruning
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StateVectors_ = StateVectors_(:,indx) ;
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end
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w_stage1 = weights(indx)./tau_tilde(indx) ;
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% draw in the new distributions
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wtilde = zeros(1,number_of_particles) ;
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for i=1:number_of_particles
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info=1 ;
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while info==1
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candidate = xparam(:,i) + chol_sigma_bar*randn(number_of_parameters,1) ;
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if all(candidate >= bounds.lb) && all(candidate <= bounds.ub)
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% model resolution for new parameters particles
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[ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
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solve_model_for_online_filter(t,candidate,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults) ;
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if info==0
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xparam(:,i) = candidate ;
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steadystate = ReducedForm.steadystate;
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state_variables_steady_state = ReducedForm.state_variables_steady_state;
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% Set local state space model (second order approximation).
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constant = ReducedForm.constant;
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ghx = ReducedForm.ghx;
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ghu = ReducedForm.ghu;
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ghxx = ReducedForm.ghxx;
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ghuu = ReducedForm.ghuu;
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ghxu = ReducedForm.ghxu;
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% Get covariance matrices and structural shocks
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epsilon = chol(ReducedForm.Q)'*randn(number_of_structural_innovations,1) ;
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% compute particles likelihood contribution
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yhat = bsxfun(@minus,StateVectors(:,i),state_variables_steady_state);
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if pruning
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yhat_ = bsxfun(@minus,StateVectors_(:,i),state_variables_steady_state);
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[tmp, tmp_] = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,DynareOptions.threads.local_state_space_iteration_2);
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StateVectors_(:,i) = tmp_(mf0,:) ;
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else
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tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,DynareOptions.threads.local_state_space_iteration_2);
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end
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StateVectors(:,i) = tmp(mf0,:) ;
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PredictionError = bsxfun(@minus,Y(t,:)',tmp(mf1,:));
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wtilde(i) = w_stage1(i)*exp(-.5*(const_lik+log(det(ReducedForm.H))+sum(PredictionError.*(ReducedForm.H\PredictionError),1)));
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end
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end
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end
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end
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% normalization
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@ -266,6 +278,10 @@ median_param = median_xparam(:,sample_size) ;
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TeX = DynareOptions.TeX;
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[nbplt,nr,nc,lr,lc,nstar] = pltorg(number_of_parameters);
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nr = ceil(sqrt(number_of_parameters)) ;
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nc = floor(sqrt(number_of_parameters));
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nbplt = 1 ;
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if TeX
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fidTeX = fopen([Model.fname '_param_traj.tex'],'w');
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@ -282,10 +298,9 @@ for plt = 1:nbplt,
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TeXNAMES = [];
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end
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hh = dyn_figure(DynareOptions.nodisplay,'Name','Parameters Trajectories');
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for k=1:min(nstar,length(xparam)-(plt-1)*nstar)
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for k=1:length(xparam)
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subplot(nr,nc,k)
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kk = (plt-1)*nstar+k;
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[name,texname] = get_the_name(kk,TeX,Model,EstimatedParameters,DynareOptions);
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[name,texname] = get_the_name(k,TeX,Model,EstimatedParameters,DynareOptions);
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if TeX
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if isempty(NAMES)
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NAMES = name;
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@ -295,7 +310,7 @@ for plt = 1:nbplt,
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TeXNAMES = char(TeXNAMES,texname);
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end
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end
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y = [mean_xparam(kk,:)' median_xparam(kk,:)' lb95_xparam(kk,:)' ub95_xparam(kk,:)' xparam(kk)*ones(sample_size,1)] ;
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y = [mean_xparam(k,:)' median_xparam(k,:)' lb95_xparam(k,:)' ub95_xparam(k,:)' xparam(k)*ones(sample_size,1)] ;
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plot(z,y);
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hold on
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title(name,'interpreter','none')
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@ -307,7 +322,7 @@ for plt = 1:nbplt,
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if TeX
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% TeX eps loader file
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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for jj = 1:min(nstar,length(x)-(plt-1)*nstar)
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for jj = 1:length(x)
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fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
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end
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fprintf(fidTeX,'\\centering \n');
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@ -329,10 +344,9 @@ for plt = 1:nbplt,
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TeXNAMES = [];
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end
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hh = dyn_figure(DynareOptions.nodisplay,'Name','Parameters Densities');
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for k=1:min(nstar,length(xparam)-(plt-1)*nstar)
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for k=1:length(xparam)
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subplot(nr,nc,k)
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kk = (plt-1)*nstar+k;
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[name,texname] = get_the_name(kk,TeX,Model,EstimatedParameters,DynareOptions);
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[name,texname] = get_the_name(k,TeX,Model,EstimatedParameters,DynareOptions);
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if TeX
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if isempty(NAMES)
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NAMES = name;
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@ -342,8 +356,8 @@ for plt = 1:nbplt,
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TeXNAMES = char(TeXNAMES,texname);
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end
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end
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optimal_bandwidth = mh_optimal_bandwidth(distrib_param(kk,:)',number_of_particles,bandwidth,kernel_function);
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[density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(kk,:)',number_of_grid_points,...
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optimal_bandwidth = mh_optimal_bandwidth(distrib_param(k,:)',number_of_particles,bandwidth,kernel_function);
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[density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(k,:)',number_of_grid_points,...
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number_of_particles,optimal_bandwidth,kernel_function);
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plot(density(:,1),density(:,2));
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hold on
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@ -356,7 +370,7 @@ for plt = 1:nbplt,
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if TeX
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% TeX eps loader file
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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for jj = 1:min(nstar,length(x)-(plt-1)*nstar)
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for jj = 1:length(x)
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fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
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end
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fprintf(fidTeX,'\\centering \n');
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