parent
497c6bd1b1
commit
3895da48a1
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@ -74,7 +74,7 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
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df = nan(size(oo_.mom.data_moments,1),length(xparam));
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end
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else
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df = nan(1,length(xparam));
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df = nan(length(xparam),1);
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end
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end
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end
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@ -284,9 +284,9 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
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df(:,jp) = dresiduals;
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end
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else
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df(:,jp) = dresiduals'*residuals + residuals'*dresiduals;
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df(jp,1) = dresiduals'*residuals + residuals'*dresiduals;
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if options_mom_.mom.penalized_estimator
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df(:,jp)=df(:,jp)+(dxparam1(:,jp))'/oo_.mom.prior.variance*(xparam-oo_.mom.prior.mean)+(xparam-oo_.mom.prior.mean)'/oo_.mom.prior.variance*(dxparam1(:,jp));
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df(jp,1)=df(jp,1)+(dxparam1(:,jp))'/oo_.mom.prior.variance*(xparam-oo_.mom.prior.mean)+(xparam-oo_.mom.prior.mean)'/oo_.mom.prior.variance*(dxparam1(:,jp));
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end
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end
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end
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@ -263,7 +263,7 @@ switch minimizer_algorithm
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Verbose = 0;
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end
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% Set flag for analytical gradient.
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if options_.analytic_derivation
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if options_.analytic_derivation || (isfield(options_,'mom') && options_.mom.analytic_jacobian==1)
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analytic_grad=1;
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else
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analytic_grad=[];
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@ -276,7 +276,7 @@ switch minimizer_algorithm
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if isempty(prior_information) %mr_hessian requires it, but can be NaN
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prior_information.p2=NaN(n_params,1);
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end
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if options_.analytic_derivation
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if options_.analytic_derivation
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old_analytic_derivation = options_.analytic_derivation;
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options_.analytic_derivation=-1; %force analytic outer product gradient hessian for each iteration
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analytic_grad=1;
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