Changed dynare_estimation_1 conformably with changes in DsgeLikelihood.
Stéphane Adjemian (Scylla)
parent
7fd6b3efc0
commit
568cc83633
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@ -98,7 +98,7 @@ else
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end
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%% compute sample moments if needed (bvar-dsge)
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% compute sample moments if needed (bvar-dsge)
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if options_.dsge_var
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if dataset_.missing.state
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error('I cannot estimate a DSGE-VAR model with missing observations!')
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@ -117,11 +117,6 @@ if options_.dsge_var
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end
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end
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%
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missing_value = dataset_.missing.state; %~(number_of_observations == gend*n_varobs);
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number_of_observations = gend*n_varobs;
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[data_index,junk,no_more_missing_observations] = ...
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describe_missing_data(data);
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oo_ = initial_estimation_checks(xparam1,dataset_,M_,estim_params_,options_,bayestopt_,oo_);
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@ -156,8 +151,7 @@ if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && options_.m
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eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
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end
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end
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return;
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return
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end
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if isequal(options_.mode_compute,6)
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@ -168,11 +162,6 @@ end
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%% Estimation of the posterior mode or likelihood mode
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if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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if ~options_.dsge_var
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fh=str2func('DsgeLikelihood');
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else
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fh=str2func('DsgeVarLikelihood');
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end
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switch options_.mode_compute
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case 1
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optim_options = optimset('display','iter','LargeScale','off', ...
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@ -180,13 +169,8 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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if isfield(options_,'optim_opt')
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eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
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end
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if ~options_.dsge_var
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[xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
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fmincon(fh,xparam1,[],[],[],[],lb,ub,[],optim_options,gend,data,data_index,number_of_observations,no_more_missing_observations);
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else
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[xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
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fmincon(fh,xparam1,[],[],[],[],lb,ub,[],optim_options,gend);
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end
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fmincon(objective_function,xparam1,[],[],[],[],lb,ub,[],optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 2
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error('ESTIMATION: mode_compute=2 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
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case 3
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@ -194,27 +178,15 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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if isfield(options_,'optim_opt')
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eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
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end
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if ~options_.dsge_var
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[xparam1,fval,exitflag] = fminunc(fh,xparam1,optim_options,gend,data,data_index,number_of_observations,no_more_missing_observations);
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else
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[xparam1,fval,exitflag] = fminunc(fh,xparam1,optim_options,gend);
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end
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[xparam1,fval,exitflag] = fminunc(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 4
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H0 = 1e-4*eye(nx);
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crit = 1e-7;
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nit = 1000;
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verbose = 2;
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if ~options_.dsge_var
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[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
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csminwel1('DsgeLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,gend,data,data_index,number_of_observations,no_more_missing_observations);
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csminwel1(objective_function,xparam1,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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disp(sprintf('Objective function at mode: %f',fval))
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disp(sprintf('Objective function at mode: %f',DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)))
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else
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[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
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csminwel1('DsgeVarLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,gend);
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disp(sprintf('Objective function at mode: %f',fval))
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disp(sprintf('Objective function at mode: %f',DsgeVarLikelihood(xparam1,gend)))
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end
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case 5
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if isfield(options_,'hess')
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flag = options_.hess;
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@ -241,14 +213,11 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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else
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[xparam1,hh,gg,fval,invhess] = newrat('DsgeVarLikelihood',xparam1,hh,gg,igg,crit,nit,flag,gend);
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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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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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if ~options_.dsge_var
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fval = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
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else
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fval = DsgeVarLikelihood(xparam1,gend);
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end
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fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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OldMode = fval;
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if ~exist('MeanPar','var')
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MeanPar = xparam1;
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@ -281,16 +250,9 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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else
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flag = 'LastCall';
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end
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if ~options_.dsge_var
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[xparam1,PostVar,Scale,PostMean] = ...
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gmhmaxlik('DsgeLikelihood',xparam1,[lb ub],options_.Opt6Numb,Scale,flag,MeanPar,CovJump,gend,data,...
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data_index,number_of_observations,no_more_missing_observations);
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fval = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
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else
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[xparam1,PostVar,Scale,PostMean] = ...
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gmhmaxlik('DsgeVarLikelihood',xparam1,[lb ub],options_.Opt6Numb,Scale,flag,MeanPar,CovJump,gend);
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fval = DsgeVarLikelihood(xparam1,gend);
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end
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[xparam1,PostVar,Scale,PostMean] = ...
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gmhmaxlik(objective_function,xparam1,[lb ub],options_.Opt6Numb,Scale,flag,MeanPar,CovJump,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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options_.mh_jscale = Scale;
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mouvement = max(max(abs(PostVar-OldPostVar)));
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disp(' ')
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@ -307,17 +269,10 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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else
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flag = 'LastCall';
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end
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if ~options_.dsge_var
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[xparam1,PostVar,Scale,PostMean] = ...
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gmhmaxlik('DsgeLikelihood',xparam1,[lb ub],...
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options_.Opt6Numb,Scale,flag,PostMean,PostVar,gend,data,data_index,number_of_observations,no_more_missing_observations);
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fval = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
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else
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[xparam1,PostVar,Scale,PostMean] = ...
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gmhmaxlik('DsgeVarLikelihood',xparam1,[lb ub],...
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options_.Opt6Numb,Scale,flag,PostMean,PostVar,gend);
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fval = DsgeVarLikelihood(xparam1,gend);
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end
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[xparam1,PostVar,Scale,PostMean] = ...
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gmhmaxlik(objective_function,xparam1,[lb ub],...
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options_.Opt6Numb,Scale,flag,PostMean,PostVar,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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options_.mh_jscale = Scale;
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mouvement = max(max(abs(PostVar-OldPostVar)));
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fval = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
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@ -328,32 +283,24 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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hh = inv(PostVar);
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save([M_.fname '_mode.mat'],'xparam1','hh');
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save([M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
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bayestopt_.jscale = ones(length(xparam1),1)*Scale;%??!
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bayestopt_.jscale = ones(length(xparam1),1)*Scale;
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end
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case 7
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% Matlab's simplex (Optimization toolbox needed).
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optim_options = optimset('display','iter','MaxFunEvals',1000000,'MaxIter',6000,'TolFun',1e-8,'TolX',1e-6);
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if isfield(options_,'optim_opt')
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eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
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end
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if ~options_.dsge_var
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[xparam1,fval,exitflag] = fminsearch(fh,xparam1,optim_options,gend,data,data_index,number_of_observations,no_more_missing_observations);
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else
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[xparam1,fval,exitflag] = fminsearch(fh,xparam1,optim_options,gend);
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end
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[xparam1,fval,exitflag] = fminsearch(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 8
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% Dynare implementation of the simplex algorithm
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if ~options_.dsge_var
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[xparam1,fval,exitflag] = simplex_optimization_routine(fh,xparam1,options_.simplex,gend,data,data_index,number_of_observations,no_more_missing_observations);
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else
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[xparam1,fval,exitflag] = simplex_optimization_routine(fh,xparam1,options_.simplex,gend);
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end
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% Dynare implementation of the simplex algorithm.
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[xparam1,fval,exitflag] = simplex_optimization-routine(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 101
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myoptions=soptions;
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myoptions(2)=1e-6; %accuracy of argument
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myoptions(3)=1e-6; %accuracy of function (see Solvopt p.29)
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myoptions(5)= 1.0;
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[xparam1,fval]=solvopt(xparam1,fh,[],myoptions,gend,data);
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[xparam1,fval]=solvopt(xparam1,objective_function,[],myoptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 102
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%simulating annealing
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% LB=zeros(size(xparam1))-20;
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@ -389,38 +336,21 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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disp([LB xparam1 UB]);
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disp(['c vector ' num2str(c')]);
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% keyboard
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if ~options_.dsge_var
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[xparam1, fval, nacc, nfcnev, nobds, ier, t, vm] = sa(fh,xparam1,maxy,rt_,epsilon,ns,nt ...
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,neps,maxevl,LB,UB,c,idisp ,t,vm,gend,data,data_index,number_of_observations,no_more_missing_observations);
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else
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[xparam1, fval, nacc, nfcnev, nobds, ier, t, vm] = sa(fh,xparam1,maxy,rt_,epsilon,ns,nt ...
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,neps,maxevl,LB,UB,c,idisp ,t,vm,gend);
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end
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[xparam1, fval, nacc, nfcnev, nobds, ier, t, vm] = sa(objective_function,xparam1,maxy,rt_,epsilon,ns,nt ...
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,neps,maxevl,LB,UB,c,idisp ,t,vm,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 'prior'
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hh = diag(bayestopt_.p2.^2);
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otherwise
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if ischar(options_.mode_compute)
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if options_.dsge_var
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[xparam1, fval, retcode ] = feval(options_.mode_compute,fh,xparam1,gend,data);
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else
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[xparam1, fval, retcode ] = feval(options_.mode_compute, ...
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fh,xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
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end
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[xparam1, fval, retcode ] = feval(options_.mode_compute,objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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else
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error(['ESTIMATION: mode_compute=' int2str(options_.mode_compute) ...
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' option is unknown!'])
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error(['dynare_estimation:: mode_compute = ' int2str(options_.mode_compute) ' option is unknown!'])
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end
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end
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if ~isequal(options_.mode_compute,6) && ~isequal(options_.mode_compute,'prior')
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if options_.cova_compute == 1
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if ~options_.dsge_var
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hh = reshape(hessian('DsgeLikelihood',xparam1, ...
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options_.gstep,gend,data,data_index,number_of_observations,...
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no_more_missing_observations),nx,nx);
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else
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hh = reshape(hessian('DsgeVarLikelihood',xparam1,options_.gstep,gend),nx,nx);
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end
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hh = reshape(hessian(objective_function,xparam1, ...
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options_.gstep,dataset_,options_,M_,estim_params_,bayestopt_,oo_),nx,nx);
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end
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end
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parameter_names = bayestopt_.name;
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