diff --git a/matlab/+mom/objective_function.m b/matlab/+mom/objective_function.m
index 6562f96628eab12bd9f6060ff7606018f2808314..1821cc566d56a5a142ed3b84fd95c4d7eb872ff8 100644
--- a/matlab/+mom/objective_function.m
+++ b/matlab/+mom/objective_function.m
@@ -221,14 +221,10 @@ if strcmp(options_mom_.mom.mom_method,'SMM')
y_sim = simult_(M_, options_mom_, dr.ys, dr, scaled_shock_series, options_mom_.order);
% provide meaningful penalty if data is nan or inf
if any(any(isnan(y_sim))) || any(any(isinf(y_sim)))
- if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
- fval = Inf(size(weighting_info.Sw,1),1);
- else
- fval = Inf;
- end
info(1)=180;
info(4) = 0.1;
exit_flag = 0;
+ fval = Inf;
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(options_mom_.mom.mom_nbr,1)*options_mom_.huge_number;
end
@@ -272,21 +268,21 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
end
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
if options_mom_.mom.penalized_estimator
- dxparam1 = eye(length(xparam));
+ dxparam = eye(length(xparam));
end
for jp=1:length(xparam)
dmoments_difference = - model_moments_params_derivs(:,jp);
dresiduals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*weighting_info.Sw*dmoments_difference;
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
if options_mom_.mom.penalized_estimator
- df(:,jp)=[dresiduals;dxparam1(:,jp)./sqrt(diag(diag(bayestopt_.p2.^2)))];
+ df(:,jp)=[dresiduals;dxparam(:,jp)./sqrt(diag(diag(bayestopt_.p2.^2)))];
else
df(:,jp) = dresiduals;
end
else
df(jp,1) = dresiduals'*residuals + residuals'*dresiduals;
if options_mom_.mom.penalized_estimator
- df(jp,1)=df(jp,1)+(dxparam1(:,jp))'/(diag(bayestopt_.p2.^2))*(xparam-bayestopt_.p1)+(xparam-bayestopt_.p1)'/(diag(bayestopt_.p2.^2))*(dxparam1(:,jp));
+ df(jp,1)=df(jp,1)+(dxparam(:,jp))'/(diag(bayestopt_.p2.^2))*(xparam-bayestopt_.p1)+(xparam-bayestopt_.p1)'/(diag(bayestopt_.p2.^2))*(dxparam(:,jp));
end
end
end
diff --git a/matlab/+mom/set_correct_bounds_for_stderr_corr.m b/matlab/+mom/set_correct_bounds_for_stderr_corr.m
index c8299c3cf70b9bf5b6af40b5ad65c139b283c178..3499cc399328e8fe683c603e85a5d9ce0b0c5ba8 100644
--- a/matlab/+mom/set_correct_bounds_for_stderr_corr.m
+++ b/matlab/+mom/set_correct_bounds_for_stderr_corr.m
@@ -8,7 +8,7 @@ function BoundsInfo = set_correct_bounds_for_stderr_corr(estim_params_,BoundsInf
% o BoundsInfo [struct] information on bounds
% -------------------------------------------------------------------------
% OUTPUT
-% o BoundsInfo [struct] updated information on bounds
+% o BoundsInfo [struct] updated bounds
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
diff --git a/matlab/+mom/transform_prior_to_laplace_prior.m b/matlab/+mom/transform_prior_to_laplace_prior.m
index db6be256aa79204be81a66580d07965307cc9611..358f9fd1e64638339a5f8b1e43c4ea40a48a094b 100644
--- a/matlab/+mom/transform_prior_to_laplace_prior.m
+++ b/matlab/+mom/transform_prior_to_laplace_prior.m
@@ -1,10 +1,10 @@
function bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
-% function bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
+% bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
% -------------------------------------------------------------------------
% Transforms the prior specification to a Laplace type of approximation:
% only the prior mean and standard deviation are relevant, all other shape
% information, except for the parameter bounds, is ignored.
-% =========================================================================
+% -------------------------------------------------------------------------
% INPUTS
% bayestopt_ [structure] prior information
% -------------------------------------------------------------------------
@@ -32,7 +32,6 @@ function bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-
if any(setdiff([0;bayestopt_.pshape],[0,3]))
fprintf('\nNon-normal priors specified. Penalized estimation uses a Laplace type of approximation:');
fprintf('\nOnly the prior mean and standard deviation are relevant, all other shape information, except for the parameter bounds, is ignored.\n\n');
diff --git a/matlab/estimation/check_posterior_sampler_options.m b/matlab/estimation/check_posterior_sampler_options.m
index c0d4235ff1f22091c40ca8019036780afacbeb2d..29de7cf6891de044ffe52e957f037e37262597fa 100644
--- a/matlab/estimation/check_posterior_sampler_options.m
+++ b/matlab/estimation/check_posterior_sampler_options.m
@@ -311,7 +311,7 @@ if init
end
% slice posterior sampler does not require mode or hessian to run
- % needs to be set to 1 to skip parts in dynare_estimation_1.m
+ % needs to be set to 1 to skip parts in dynare_estimation_1.m or mom.run.m
% requiring posterior maximization/calibrated smoother before MCMC
options_.mh_posterior_mode_estimation=true;
diff --git a/matlab/estimation/posterior_sampler.m b/matlab/estimation/posterior_sampler.m
index eb2b55ea8d47e1ab0ffdf5e246928174b9b6ba98..f1939042bacd97f2fa0f6b193cb22bad03d4d24a 100644
--- a/matlab/estimation/posterior_sampler.m
+++ b/matlab/estimation/posterior_sampler.m
@@ -10,8 +10,8 @@ function posterior_sampler(TargetFun,ProposalFun,xparam1,sampler_options,mh_boun
% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
% o sampler_options structure
% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
-% o dataset_ [structure] data structure
-% o dataset_info [structure] dataset info structure
+% o dataset_ [structure] data structure (likelihood-based) or data moments (method of moments)
+% o dataset_info [structure] dataset info structure (likelihood-based) or info on weighting matrix (method of moments)
% o options_ [structure] options structure
% o M_ [structure] model structure
% o estim_params_ [structure] estimated parameters structure
diff --git a/matlab/estimation/posterior_sampler_initialization.m b/matlab/estimation/posterior_sampler_initialization.m
index 1307588241be140b05f78e4b6e5494602ef4bce5..194f4c3c30f1401043e3157d734ecae1a143fd33 100644
--- a/matlab/estimation/posterior_sampler_initialization.m
+++ b/matlab/estimation/posterior_sampler_initialization.m
@@ -9,8 +9,8 @@ function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npa
% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
% o vv [double] (p*p) matrix, posterior covariance matrix (at the mode).
% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
-% o dataset_ data structure
-% o dataset_info dataset info structure
+% o dataset_ data structure (likelihood based) or data moments/irfs (method of moments)
+% o dataset_info dataset info structure (likelihood based) or info on weighting matrix (method of moments)
% o options_ options structure
% o M_ model structure
% o estim_params_ estimated parameters structure
diff --git a/matlab/utilities/estimation/tune_mcmc_mh_jscale_wrapper.m b/matlab/utilities/estimation/tune_mcmc_mh_jscale_wrapper.m
index 0da796f513cd6ebbf74e77a49be771094ff4eb35..82548cd5841fe74447bf1e9ff66ce2daac983cfe 100644
--- a/matlab/utilities/estimation/tune_mcmc_mh_jscale_wrapper.m
+++ b/matlab/utilities/estimation/tune_mcmc_mh_jscale_wrapper.m
@@ -18,6 +18,7 @@ function mh_jscale = tune_mcmc_mh_jscale_wrapper(invhess, options_, M_, objectiv
% -------------------------------------------------------------------------
% This function is called by
% o dynare_estimation_1
+% o mom.run
% -------------------------------------------------------------------------
% Copyright © 2023 Dynare Team