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method_of_moments: add warnings for problematic prior stderr/corr and value 

Also moved oo_.prior to oo_.mom.prior
kalman-mex
Willi Mutschler 2023-09-04 16:10:09 +02:00
parent 017b4f05f6
commit 0cd65df72a
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GPG Key ID: 91E724BF17A73F6D
2 changed files with 17 additions and 4 deletions
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8
matlab/+mom/objective_function.m
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@ -263,12 +263,12 @@ oo_.mom.Q = residuals'*residuals;
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = residuals;
if options_mom_.mom.penalized_estimator
fval=[fval;(xparam1-oo_.prior.mean)./sqrt(diag(oo_.prior.variance))];
fval=[fval;(xparam1-oo_.mom.prior.mean)./sqrt(diag(oo_.mom.prior.variance))];
end
else
fval = oo_.mom.Q;
if options_mom_.mom.penalized_estimator
fval=fval+(xparam1-oo_.prior.mean)'/oo_.prior.variance*(xparam1-oo_.prior.mean);
fval=fval+(xparam1-oo_.mom.prior.mean)'/oo_.mom.prior.variance*(xparam1-oo_.mom.prior.mean);
end
end
@ -283,14 +283,14 @@ if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
if options_mom_.mom.penalized_estimator
df(:,jp)=[dresiduals;dxparam1(:,jp)./sqrt(diag(oo_.prior.variance))];
df(:,jp)=[dresiduals;dxparam1(:,jp)./sqrt(diag(oo_.mom.prior.variance))];
else
df(:,jp) = dresiduals;
end
else
df(:,jp) = dresiduals'*residuals + residuals'*dresiduals;
if options_mom_.mom.penalized_estimator
df(:,jp)=df(:,jp)+(dxparam1(:,jp))'/oo_.prior.variance*(xparam1-oo_.prior.mean)+(xparam1-oo_.prior.mean)'/oo_.prior.variance*(dxparam1(:,jp));
df(:,jp)=df(:,jp)+(dxparam1(:,jp))'/oo_.mom.prior.variance*(xparam1-oo_.mom.prior.mean)+(xparam1-oo_.mom.prior.mean)'/oo_.mom.prior.variance*(dxparam1(:,jp));
end
end
end

13
matlab/+mom/run.m
View File

@ -400,6 +400,19 @@ M_ = set_all_parameters(xparam0,estim_params_,M_);
% provide warning if there is NaN in parameters
test_for_deep_parameters_calibration(M_);
if doBayesianEstimation
% warning if prior allows that stderr parameters are negative or corr parameters are outside the unit circle
check_prior_stderr_corr(estim_params_,bayestopt_);
% check value of prior density
[~,~,~,info]= priordens(xparam0,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
if any(info)
fprintf('The prior density evaluated at the initial values is Inf for the following parameters: %s\n',bayestopt_.name{info,1})
error('The initial value of the prior is -Inf!')
end
end
% -------------------------------------------------------------------------
% Step 4: Checks and transformations for data
% -------------------------------------------------------------------------