diff --git a/doc/parallel/parallel.tex b/doc/parallel/parallel.tex
index 849efe4c9ceb4aabe21df235c76e3d535648399d..dd3ea6864dea17840d47c69015cd01629a1e888c 100644
--- a/doc/parallel/parallel.tex
+++ b/doc/parallel/parallel.tex
@@ -189,7 +189,7 @@ We have considered the following DYNARE components suitable to be parallelized u
\item the Random Walk- (and the analogous Independent-)-Metropolis-Hastings algorithm with multiple chains: the different chains are completely independent and do not require any communication between them, so it can be executed on different cores/CPUs/Computer Network easily;
\item a number of procedures performed after the completion of Metropolis, that use the posterior MC sample:
\begin{enumerate}
-\item the diagnostic tests for the convergence of the Markov Chain \\(\texttt{McMCDiagnostics.m});
+\item the diagnostic tests for the convergence of the Markov Chain \\(\texttt{mcmc\_diagnostics.m});
\item the function that computes posterior IRF's (\texttt{posteriorIRF.m}).
\item the function that computes posterior statistics for filtered and smoothed variables, forecasts, smoothed shocks, etc.. \\ (\verb"prior_posterior_statistics.m").
\item the utility function that loads matrices of results and produces plots for posterior statistics (\texttt{pm3.m}).
@@ -725,8 +725,8 @@ So far, we have parallelized the following functions, by selecting the most comp
\verb"independent_metropolis_hastings.m", \\
\verb"independent_metropolis_hastings_core.m";
\item the cycle looping over estimated parameters computing univariate diagnostics:\\
-\verb"McMCDiagnostics.m", \\
-\verb"McMCDiagnostics_core.m";
+\verb"mcmc_diagnostics.m", \\
+\verb"mcmc_diagnostics_core.m";
\item the Monte Carlo cycle looping over posterior parameter subdraws performing the IRF simulations (\verb"<*>_core1") and the cycle looping over exogenous shocks plotting IRF's charts (\verb"<*>_core2"):\\
\verb"posteriorIRF.m", \\\verb"posteriorIRF_core1.m", \verb"posteriorIRF_core2.m";
\item the Monte Carlo cycle looping over posterior parameter subdraws, that computes filtered, smoothed, forecasted variables and shocks:\\
diff --git a/matlab/+mom/Jtest.m b/matlab/+mom/Jtest.m
new file mode 100644
index 0000000000000000000000000000000000000000..0cf41df5dd8b51189662d5e53fd78a21af20a933
--- /dev/null
+++ b/matlab/+mom/Jtest.m
@@ -0,0 +1,67 @@
+function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, nobs)
+% function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, nobs)
+% -------------------------------------------------------------------------
+% Computes the J-test statistic and p-value for a GMM/SMM estimation
+% =========================================================================
+% INPUTS
+% xparam: [vector] estimated parameter vector
+% objective_function: [function handle] objective function
+% Woptflag: [logical] flag if optimal weighting matrix has already been computed
+% oo_: [struct] results
+% options_mom_: [struct] options
+% bayestopt_: [struct] information on priors
+% Bounds: [struct] bounds on parameters
+% estim_params_: [struct] information on estimated parameters
+% M_: [struct] information on the model
+% nobs: [scalar] number of observations
+% -------------------------------------------------------------------------
+% OUTPUT
+% oo_: [struct] updated results
+% -------------------------------------------------------------------------
+% This function is called by
+% o mom.run
+% -------------------------------------------------------------------------
+% This function calls
+% o mom.objective_function
+% o mom.optimal_weighting_matrix
+% =========================================================================
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+if options_mom_.mom.mom_nbr > length(xparam)
+ % Get optimal weighting matrix for J test, if necessary
+ if ~Woptflag
+ W_opt = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
+ oo_J = oo_;
+ oo_J.mom.Sw = chol(W_opt);
+ fval = feval(objective_function, xparam, Bounds, oo_J, estim_params_, M_, options_mom_, bayestopt_);
+ else
+ fval = oo_.mom.Q;
+ end
+ % Compute J statistic
+ if strcmp(options_mom_.mom.mom_method,'SMM')
+ Variance_correction_factor = options_mom_.mom.variance_correction_factor;
+ elseif strcmp(options_mom_.mom.mom_method,'GMM')
+ Variance_correction_factor = 1;
+ end
+ oo_.mom.J_test.j_stat = nobs*Variance_correction_factor*fval/options_mom_.mom.weighting_matrix_scaling_factor;
+ oo_.mom.J_test.degrees_freedom = length(oo_.mom.model_moments)-length(xparam);
+ oo_.mom.J_test.p_val = 1-chi2cdf(oo_.mom.J_test.j_stat, oo_.mom.J_test.degrees_freedom);
+ fprintf('\nValue of J-test statistic: %f\n',oo_.mom.J_test.j_stat);
+ fprintf('p-value of J-test statistic: %f\n',oo_.mom.J_test.p_val);
+end
\ No newline at end of file
diff --git a/matlab/+mom/default_option_mom_values.m b/matlab/+mom/default_option_mom_values.m
new file mode 100644
index 0000000000000000000000000000000000000000..c0bc017d24f53fdc3990b3833c3b9b3376d2d6e4
--- /dev/null
+++ b/matlab/+mom/default_option_mom_values.m
@@ -0,0 +1,270 @@
+function options_mom_ = default_option_mom_values(options_mom_, options_, dname, doBayesianEstimation)
+% function options_mom_ = default_option_mom_values(options_mom_, options_, dname, doBayesianEstimation)
+
+% Returns structure containing the options for method_of_moments command
+
+% options_mom_ is local and contains default and user-specified values for
+% all settings needed for the method of moments estimation. Some options,
+% though, are set by the preprocessor into options_ and we copy these over.
+% The idea is to be independent of options_ and have full control of the
+% estimation instead of possibly having to deal with options chosen somewhere
+% else in the mod file.
+
+% =========================================================================
+% INPUTS
+% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
+% o options_: [structure] information on global options
+% o dname: [string] name of directory to store results
+% o doBayesianEstimation [boolean] indicator whether we do Bayesian estimation
+% -------------------------------------------------------------------------
+% OUTPUTS
+% o oo_: [structure] storage for results (oo_)
+% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
+% -------------------------------------------------------------------------
+% This function is called by
+% o mom.run
+% -------------------------------------------------------------------------
+% This function calls
+% o set_default_option
+% o user_has_matlab_license
+% o user_has_octave_forge_package
+% -------------------------------------------------------------------------
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+
+mom_method = options_mom_.mom.mom_method; % this is a required option
+
+% -------------------------------------------------------------------------
+% LIMITATIONS
+% -------------------------------------------------------------------------
+
+if options_.logged_steady_state || options_.loglinear
+ error('method_of_moments: The loglinear option is not supported. Please append the required logged variables as auxiliary equations.')
+else
+ options_mom_.logged_steady_state = 0;
+ options_mom_.loglinear = false;
+end
+options_mom_.hessian.use_penalized_objective = false; % penalized objective not yet
+% options related to variable declarations
+if isfield(options_,'trend_coeffs')
+ error('method_of_moments: %s does not allow for trend in data',mom_method)
+end
+% options related to endogenous prior restrictions are not supported
+if ~isempty(options_.endogenous_prior_restrictions.irf) && ~isempty(options_.endogenous_prior_restrictions.moment)
+ fprintf('method_of_moments: Endogenous prior restrictions are not supported yet and will be skipped.\n')
+end
+options_mom_.endogenous_prior_restrictions.irf = {};
+options_mom_.endogenous_prior_restrictions.moment = {};
+
+options_mom_.mom.analytic_jacobian_optimizers = [1, 3, 4, 13, 101]; % these are currently supported optimizers that are able to use the analytical_jacobian option
+
+% -------------------------------------------------------------------------
+% OPTIONS POSSIBLY SET BY THE USER
+% -------------------------------------------------------------------------
+
+% common settings
+options_mom_ = set_default_option(options_mom_,'dirname',dname); % specify directory in which to store estimation output
+options_mom_ = set_default_option(options_mom_,'graph_format','eps'); % specify the file format(s) for graphs saved to disk
+options_mom_ = set_default_option(options_mom_,'nodisplay',false); % do not display the graphs, but still save them to disk
+options_mom_ = set_default_option(options_mom_,'nograph',false); % do not create graphs (which implies that they are not saved to the disk nor displayed)
+options_mom_ = set_default_option(options_mom_,'noprint',false); % do not print output to console
+options_mom_ = set_default_option(options_mom_,'TeX',false); % print TeX tables and graphics
+options_mom_.mom = set_default_option(options_mom_.mom,'verbose',false); % display and store intermediate estimation results
+%options_mom_ = set_default_option(options_mom_,'verbosity',false); %
+if doBayesianEstimation
+ options_mom_ = set_default_option(options_mom_,'plot_priors',true); % control plotting of priors
+ options_mom_ = set_default_option(options_mom_,'prior_trunc',1e-10); % probability of extreme values of the prior density that is ignored when computing bounds for the parameters
+end
+
+% specific method_of_moments settings
+if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
+ options_mom_.mom = set_default_option(options_mom_.mom,'bartlett_kernel_lag',20); % bandwith in optimal weighting matrix
+ options_mom_.mom = set_default_option(options_mom_.mom,'penalized_estimator',false); % include deviation from prior mean as additional moment restriction and use prior precision as weights
+ options_mom_.mom = set_default_option(options_mom_.mom,'se_tolx',1e-5); % step size for numerical computation of standard errors
+ options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix_scaling_factor',1); % scaling of weighting matrix in objective function
+ options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix',{'DIAGONAL'; 'OPTIMAL'}); % weighting matrix in moments distance objective function at each iteration of estimation;
+ % possible values are 'OPTIMAL', 'IDENTITY_MATRIX' ,'DIAGONAL' or a filename. Size of cell determines stages in iterated estimation.
+end
+if strcmp(mom_method,'SMM')
+ options_mom_.mom = set_default_option(options_mom_.mom,'burnin',500); % number of periods dropped at beginning of simulation
+ options_mom_.mom = set_default_option(options_mom_.mom,'bounded_shock_support',false); % trim shocks in simulation to +- 2 stdev
+ options_mom_.mom = set_default_option(options_mom_.mom,'seed',24051986); % seed used in simulations
+ options_mom_.mom = set_default_option(options_mom_.mom,'simulation_multiple',7); % multiple of the data length used for simulation
+end
+if strcmp(mom_method,'GMM')
+ options_mom_.mom = set_default_option(options_mom_.mom,'analytic_standard_errors',false); % compute standard errors numerically (0) or analytically (1). Analytical derivatives are only available for GMM.
+end
+
+% data related options
+if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
+ options_mom_ = set_default_option(options_mom_,'first_obs',1); % number of first observation
+ options_mom_ = set_default_option(options_mom_,'logdata',false); % if data is already in logs
+ options_mom_ = set_default_option(options_mom_,'nobs',NaN); % number of observations
+ options_mom_ = set_default_option(options_mom_,'prefilter',false); % demean each data series by its empirical mean and use centered moments
+ options_mom_ = set_default_option(options_mom_,'xls_sheet',1); % name of sheet with data in Excel, Octave does not support the empty string, rather use first sheet
+ options_mom_ = set_default_option(options_mom_,'xls_range',''); % range of data in Excel sheet
+end
+
+% optimization related
+if (isoctave && user_has_octave_forge_package('optim')) || (~isoctave && user_has_matlab_license('optimization_toolbox'))
+ if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
+ options_mom_ = set_default_option(options_mom_,'mode_compute',13); % specifies lsqnonlin as default optimizer for minimization
+ end
+else
+ options_mom_ = set_default_option(options_mom_,'mode_compute',4); % specifies csminwel as fallback default option for minimization
+end
+options_mom_ = set_default_option(options_mom_,'additional_optimizer_steps',[]); % vector of additional mode-finders run after mode_compute
+options_mom_ = set_default_option(options_mom_,'optim_opt',[]); % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
+options_mom_ = set_default_option(options_mom_,'silent_optimizer',false); % run minimization of moments distance silently without displaying results or saving files in between
+options_mom_ = set_default_option(options_mom_,'huge_number',1e7); % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
+options_mom_.mom = set_default_option(options_mom_.mom,'analytic_jacobian',false); % use analytic Jacobian in optimization, only available for GMM and gradient-based optimizers
+options_mom_.optimizer_vec = [options_mom_.mode_compute;num2cell(options_mom_.additional_optimizer_steps)];
+
+% perturbation related
+options_mom_ = set_default_option(options_mom_,'order',1); % order of Taylor approximation in perturbation
+options_mom_ = set_default_option(options_mom_,'pruning',false); % use pruned state space system at order>1
+options_mom_ = set_default_option(options_mom_,'aim_solver',false); % use AIM algorithm to compute perturbation approximation instead of mjdgges
+options_mom_ = set_default_option(options_mom_,'k_order_solver',false); % use k_order_perturbation instead of mjdgges
+options_mom_ = set_default_option(options_mom_,'dr_cycle_reduction',false); % use cycle reduction algorithm to solve the polynomial equation for retrieving the coefficients associated to the endogenous variables in the decision rule
+options_mom_ = set_default_option(options_mom_,'dr_cycle_reduction_tol',1e-7); % convergence criterion used in the cycle reduction algorithm
+options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction',false); % use logarithmic reduction algorithm to solve the polynomial equation for retrieving the coefficients associated to the endogenous variables in the decision rule
+options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction_maxiter',100); % maximum number of iterations used in the logarithmic reduction algorithm
+options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction_tol',1e-12); % convergence criterion used in the cycle reduction algorithm
+options_mom_ = set_default_option(options_mom_,'qz_criterium',1-1e-6); % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
+ % if there are no unit roots one can use 1.0 (or slightly below) which we set as default; if they are possible, you may have have multiple unit roots and the accuracy decreases when computing the eigenvalues in lyapunov_symm
+ % Note that unit roots are only possible at first-order, at higher order we set it to 1 in pruned_state_space_system and focus only on stationary observables.
+options_mom_ = set_default_option(options_mom_,'qz_zero_threshold',1e-6); % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
+options_mom_ = set_default_option(options_mom_,'schur_vec_tol',1e-11); % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix.
+
+% numerical algorithms
+options_mom_ = set_default_option(options_mom_,'lyapunov_db',false); % doubling algorithm (disclyap_fast) to solve Lyapunov equation to compute variance-covariance matrix of state variables
+options_mom_ = set_default_option(options_mom_,'lyapunov_fp',false); % fixed-point algorithm to solve Lyapunov equation to compute variance-covariance matrix of state variables
+options_mom_ = set_default_option(options_mom_,'lyapunov_srs',false); % square-root-solver (dlyapchol) algorithm to solve Lyapunov equation to compute variance-covariance matrix of state variables
+options_mom_ = set_default_option(options_mom_,'lyapunov_complex_threshold',1e-15); % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
+options_mom_ = set_default_option(options_mom_,'lyapunov_fixed_point_tol',1e-10); % convergence criterion used in the fixed point Lyapunov solver
+options_mom_ = set_default_option(options_mom_,'lyapunov_doubling_tol',1e-16); % convergence criterion used in the doubling algorithm
+options_mom_ = set_default_option(options_mom_,'sylvester_fp',false); % determines whether to use fixed point algorihtm to solve Sylvester equation (gensylv_fp), faster for large scale models
+options_mom_ = set_default_option(options_mom_,'sylvester_fixed_point_tol',1e-12); % convergence criterion used in the fixed point Sylvester solver
+
+% mode check plot
+options_mom_.mode_check.nolik = false; % we don't do likelihood (also this initializes mode_check substructure)
+options_mom_.mode_check = set_default_option(options_mom_.mode_check,'status',false); % plot the target function for values around the computed minimum for each estimated parameter in turn. This is helpful to diagnose problems with the optimizer.
+options_mom_.mode_check = set_default_option(options_mom_.mode_check,'neighbourhood_size',.5); % width of the window around the computed minimum to be displayed on the diagnostic plots. This width is expressed in percentage deviation. The Inf value is allowed, and will trigger a plot over the entire domain
+options_mom_.mode_check = set_default_option(options_mom_.mode_check,'symmetric_plots',true); % ensure that the check plots are symmetric around the minimum. A value of 0 allows to have asymmetric plots, which can be useful if the minimum is close to a domain boundary, or in conjunction with neighbourhood_size = Inf when the domain is not the entire real line
+options_mom_.mode_check = set_default_option(options_mom_.mode_check,'number_of_points',20); % number of points around the minimum where the target function is evaluated (for each parameter)
+
+
+% -------------------------------------------------------------------------
+% OPTIONS THAT NEED TO BE CARRIED OVER (E.G. SET BY THE PREPROCESSOR)
+% -------------------------------------------------------------------------
+
+% related to VAROBS block
+options_mom_.varobs = options_.varobs; % observable variables in order they are declared in varobs
+options_mom_.varobs_id = options_.varobs_id; % index for observable variables in M_.endo_names
+options_mom_.obs_nbr = length(options_mom_.varobs); % number of observed variables
+
+% related to call of dynare
+options_mom_.console_mode = options_.console_mode;
+options_mom_.parallel = options_.parallel;
+options_mom_.parallel_info = options_.parallel_info;
+
+% related to estimated_params and estimated_params_init blocks
+options_mom_.use_calibration_initialization = options_.use_calibration_initialization;
+
+% related to model block
+options_mom_.linear = options_.linear;
+options_mom_.use_dll = options_.use_dll;
+options_mom_.block = options_.block;
+options_mom_.bytecode = options_.bytecode;
+
+% related to steady-state computations
+options_mom_.homotopy_force_continue = options_.homotopy_force_continue;
+options_mom_.homotopy_mode = options_.homotopy_mode;
+options_mom_.homotopy_steps = options_.homotopy_steps;
+options_mom_.markowitz = options_.markowitz;
+options_mom_.solve_algo = options_.solve_algo;
+options_mom_.solve_tolf = options_.solve_tolf;
+options_mom_.solve_tolx = options_.solve_tolx;
+options_mom_.steady = options_.steady;
+options_mom_.steadystate = options_.steadystate;
+options_mom_.steadystate_flag = options_.steadystate_flag;
+%options_mom_.steadystate_partial
+options_mom_.threads = options_.threads; % needed by resol
+options_mom_.debug = options_.debug; % debug option needed by some functions, e.g. check_plot
+
+% random numbers
+options_mom_.DynareRandomStreams.seed = options_.DynareRandomStreams.seed;
+options_mom_.DynareRandomStreams.algo = options_.DynareRandomStreams.algo;
+
+% dataset_ related
+options_mom_.dataset = options_.dataset;
+options_mom_.initial_period = options_.initial_period;
+
+% optimization related
+if any(cellfun(@(x) isnumeric(x) && any(x == 2), options_mom_.optimizer_vec)) % simulated annealing (mode_compute=2)
+ options_mom_.saopt = options_.saopt;
+end
+if any(cellfun(@(x) isnumeric(x) && any(x == 4), options_mom_.optimizer_vec)) % csminwel (mode_compute=4)
+ options_mom_.csminwel = options_.csminwel;
+end
+if any(cellfun(@(x) isnumeric(x) && any(x == 5), options_mom_.optimizer_vec)) % newrat (mode_compute=5)
+ options_mom_.newrat = options_.newrat;
+end
+if any(cellfun(@(x) isnumeric(x) && any(x == 6), options_mom_.optimizer_vec)) % gmhmaxlik (mode_compute=6)
+ options_mom_.gmhmaxlik = options_.gmhmaxlik;
+ options_mom_.mh_jscale = options_.mh_jscale;
+end
+if any(cellfun(@(x) isnumeric(x) && any(x == 8), options_mom_.optimizer_vec)) % simplex variation on Nelder Mead algorithm (mode_compute=8)
+ options_mom_.simplex = options_.simplex;
+end
+if any(cellfun(@(x) isnumeric(x) && any(x == 9), options_mom_.optimizer_vec)) % cmaes (mode_compute=9)
+ options_mom_.cmaes = options_.cmaes;
+end
+if any(cellfun(@(x) isnumeric(x) && any(x == 10), options_mom_.optimizer_vec)) % simpsa (mode_compute=10)
+ options_mom_.simpsa = options_.simpsa;
+end
+if any(cellfun(@(x) isnumeric(x) && any(x == 12), options_mom_.optimizer_vec)) % particleswarm (mode_compute=12)
+ options_mom_.particleswarm = options_.particleswarm;
+end
+if any(cellfun(@(x) isnumeric(x) && any(x == 101), options_mom_.optimizer_vec)) % solveopt (mode_compute=101)
+ options_mom_.solveopt = options_.solveopt;
+end
+if any(cellfun(@(x) isnumeric(x) && (any(x == 4) || any(x == 5)), options_mom_.optimizer_vec)) % used by csminwel and newrat
+ options_mom_.gradient_method = options_.gradient_method;
+ options_mom_.gradient_epsilon = options_.gradient_epsilon;
+end
+options_mom_.gstep = options_.gstep; % needed by hessian.m
+options_mom_.trust_region_initial_step_bound_factor = options_.trust_region_initial_step_bound_factor; % used in dynare_solve for trust_region
+
+% other
+options_mom_.MaxNumberOfBytes = options_.MaxNumberOfBytes;
+%options_mom_.MaximumNumberOfMegaBytes = options_.MaximumNumberOfMegaBytes;
+
+
+% -------------------------------------------------------------------------
+% DEFAULT VALUES
+% -------------------------------------------------------------------------
+
+options_mom_.analytic_derivation = 0;
+options_mom_.analytic_derivation_mode = 0; % needed by get_perturbation_params_derivs.m, ie use efficient sylvester equation method to compute analytical derivatives as in Ratto & Iskrev (2012)
+options_mom_.initialize_estimated_parameters_with_the_prior_mode = 0; % needed by set_prior.m
+options_mom_.figures = options_.figures; % needed by plot_priors.m
+options_mom_.ramsey_policy = false; % needed by evaluate_steady_state
+options_mom_.risky_steadystate = false; % needed by resol
+options_mom_.jacobian_flag = true; % needed by dynare_solve
\ No newline at end of file
diff --git a/matlab/+mom/display_comparison_moments.m b/matlab/+mom/display_comparison_moments.m
new file mode 100644
index 0000000000000000000000000000000000000000..bcd1584edcb2e728dd8a399babaddddfef13689d
--- /dev/null
+++ b/matlab/+mom/display_comparison_moments.m
@@ -0,0 +1,74 @@
+function display_comparison_moments(M_, options_mom_, data_moments, model_moments)
+% function display_comparison_moments(M_, options_mom_, data_moments, model_moments)
+% -------------------------------------------------------------------------
+% Displays and saves to disk the comparison of the data moments and the model moments
+% =========================================================================
+% INPUTS
+% M_: [structure] model information
+% options_mom_: [structure] method of moments options
+% data_moments: [vector] data moments
+% model_moments: [vector] model moments
+% -------------------------------------------------------------------------
+% OUTPUT
+% No output, just displays and saves to disk the comparison of the data moments and the model moments
+% -------------------------------------------------------------------------
+% This function is called by
+% o mom.run
+% -------------------------------------------------------------------------
+% This function calls
+% o dyn_latex_table
+% o dyntable
+% o cellofchararraymaxlength
+% =========================================================================
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+titl = ['Comparison of matched data moments and model moments (',options_mom_.mom.mom_method,')'];
+headers = {'Moment','Data','Model'};
+for jm = 1:size(M_.matched_moments,1)
+ lables_tmp = 'E[';
+ lables_tmp_tex = 'E \left[ ';
+ for jvar = 1:length(M_.matched_moments{jm,1})
+ lables_tmp = [lables_tmp M_.endo_names{M_.matched_moments{jm,1}(jvar)}];
+ lables_tmp_tex = [lables_tmp_tex, '{', M_.endo_names_tex{M_.matched_moments{jm,1}(jvar)}, '}'];
+ if M_.matched_moments{jm,2}(jvar) ~= 0
+ lables_tmp = [lables_tmp, '(', num2str(M_.matched_moments{jm,2}(jvar)), ')'];
+ lables_tmp_tex = [lables_tmp_tex, '_{t', num2str(M_.matched_moments{jm,2}(jvar)), '}'];
+ else
+ lables_tmp_tex = [lables_tmp_tex, '_{t}'];
+ end
+ if M_.matched_moments{jm,3}(jvar) > 1
+ lables_tmp = [lables_tmp, '^', num2str(M_.matched_moments{jm,3}(jvar))];
+ lables_tmp_tex = [lables_tmp_tex, '^{', num2str(M_.matched_moments{jm,3}(jvar)) '}'];
+ end
+ if jvar == length(M_.matched_moments{jm,1})
+ lables_tmp = [lables_tmp, ']'];
+ lables_tmp_tex = [lables_tmp_tex, ' \right]'];
+ else
+ lables_tmp = [lables_tmp, '*'];
+ lables_tmp_tex = [lables_tmp_tex, ' \times '];
+ end
+ end
+ labels{jm,1} = lables_tmp;
+ labels_TeX{jm,1} = lables_tmp_tex;
+end
+data_mat = [data_moments model_moments];
+dyntable(options_mom_, titl, headers, labels, data_mat, cellofchararraymaxlength(labels)+2, 10, 7);
+if options_mom_.TeX
+ dyn_latex_table(M_, options_mom_, titl, ['comparison_moments_', options_mom_.mom.mom_method], headers, labels_TeX, data_mat, cellofchararraymaxlength(labels)+2, 10, 7);
+end
\ No newline at end of file
diff --git a/matlab/+mom/data_moments.m b/matlab/+mom/get_data_moments.m
similarity index 90%
rename from matlab/+mom/data_moments.m
rename to matlab/+mom/get_data_moments.m
index 8845f55083c9738fa2d4192c224b35fde0bd157a..0a2ced1c42b035a1e84ccee29f3a595528269c35 100644
--- a/matlab/+mom/data_moments.m
+++ b/matlab/+mom/get_data_moments.m
@@ -1,5 +1,5 @@
-function [dataMoments, m_data] = data_moments(data, oo_, matched_moments_, options_mom_)
-% [dataMoments, m_data] = data_moments(data, oo_, matched_moments_, options_mom_)
+function [dataMoments, m_data] = get_data_moments(data, oo_, matched_moments_, options_mom_)
+% [dataMoments, m_data] = get_data_moments(data, oo_, matched_moments_, options_mom_)
% This function computes the user-selected empirical moments from data
% =========================================================================
% INPUTS
@@ -13,10 +13,10 @@ function [dataMoments, m_data] = data_moments(data, oo_, matched_moments_, optio
% o m_data [T x numMom] selected empirical moments at each point in time
% -------------------------------------------------------------------------
% This function is called by
-% o mom.run.m
-% o mom.objective_function.m
+% o mom.run
+% o mom.objective_function
% =========================================================================
-% Copyright © 2020-2021 Dynare Team
+% Copyright © 2020-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -49,7 +49,7 @@ for jm = 1:options_mom_.mom.mom_nbr
leadlags = matched_moments_{jm,2}; % lags are negative numbers and leads are positive numbers
powers = matched_moments_{jm,3};
for jv = 1:length(vars)
- jvar = (oo_.dr.obs_var == vars(jv));
+ jvar = (oo_.mom.obs_var == vars(jv));
y = NaN(T,1); %Take care of T_eff instead of T for lags and NaN via mean with 'omitnan' option below
y( (1-min(leadlags(jv),0)) : (T-max(leadlags(jv),0)), 1) = data( (1+max(leadlags(jv),0)) : (T+min(leadlags(jv),0)), jvar).^powers(jv);
if jv==1
@@ -66,7 +66,4 @@ for jm = 1:options_mom_.mom.mom_nbr
end
m_data_tmp(isnan(m_data_tmp)) = dataMoments(jm,1);
m_data(:,jm) = m_data_tmp;
-end
-
-
-end %function end
\ No newline at end of file
+end
\ No newline at end of file
diff --git a/matlab/+mom/matched_moments_block.m b/matlab/+mom/matched_moments_block.m
new file mode 100644
index 0000000000000000000000000000000000000000..a0b44c70b87b848001c744849b4fee29107c8760
--- /dev/null
+++ b/matlab/+mom/matched_moments_block.m
@@ -0,0 +1,80 @@
+function matched_moments = matched_moments_block(matched_moments, mom_method)
+% function matched_moments = matched_moments_block(matched_moments, mom_method)
+% -------------------------------------------------------------------------
+% Checks and transforms matched_moments bock for further use in the estimation
+% =========================================================================
+% INPUTS
+% matched_moments: [cell array] original matched_moments block
+% mom_method: [string] method of moments method (GMM or SMM)
+% -------------------------------------------------------------------------
+% OUTPUT
+% matched_moments: [cell array] transformed matched_moments block
+% -------------------------------------------------------------------------
+% This function is called by
+% o mom.run
+% =========================================================================
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+matched_moments_orig = matched_moments;
+% higher-order product moments not supported yet for GMM
+if strcmp(mom_method, 'GMM') && any(cellfun(@sum,matched_moments(:,3))> 2)
+ error('method_of_moments: GMM does not yet support product moments higher than 2. Change your ''matched_moments'' block!');
+end
+% check for duplicate moment conditions
+for jm = 1:size(matched_moments,1)
+ % expand powers to vector of ones
+ if any(matched_moments{jm,3}>1)
+ tmp1=[]; tmp2=[]; tmp3=[];
+ for jjm=1:length(matched_moments{jm,3})
+ tmp1 = [tmp1 repmat(matched_moments{jm,1}(jjm),[1 matched_moments{jm,3}(jjm)]) ];
+ tmp2 = [tmp2 repmat(matched_moments{jm,2}(jjm),[1 matched_moments{jm,3}(jjm)]) ];
+ tmp3 = [tmp3 repmat(1,[1 matched_moments{jm,3}(jjm)]) ];
+ end
+ matched_moments{jm,1} = tmp1;
+ matched_moments{jm,2} = tmp2;
+ matched_moments{jm,3} = tmp3;
+ end
+ % shift time structure to focus only on lags
+ matched_moments{jm,2} = matched_moments{jm,2} - max(matched_moments{jm,2});
+ % sort such that t=0 variable comes first
+ [matched_moments{jm,2},idx_sort] = sort(matched_moments{jm,2},'descend');
+ matched_moments{jm,1} = matched_moments{jm,1}(idx_sort);
+ matched_moments{jm,3} = matched_moments{jm,3}(idx_sort);
+end
+% find duplicate rows in cell array by making groups according to powers as we can then use cell2mat for the unique function
+powers = cellfun(@sum,matched_moments(:,3))';
+UniqueMomIdx = [];
+for jpow = unique(powers)
+ idx1 = find(powers==jpow);
+ [~,idx2] = unique(cell2mat(matched_moments(idx1,:)),'rows');
+ UniqueMomIdx = [UniqueMomIdx idx1(idx2)];
+end
+% remove duplicate elements
+DuplicateMoms = setdiff(1:size(matched_moments_orig,1),UniqueMomIdx);
+if ~isempty(DuplicateMoms)
+ fprintf('Duplicate declared moments found and removed in ''matched_moments'' block in rows:\n %s.\n',num2str(DuplicateMoms))
+ fprintf('Dynare will continue with remaining moment conditions\n');
+end
+if strcmp(mom_method, 'SMM')
+ % for SMM: keep the original structure, but get rid of duplicate moments
+ matched_moments = matched_moments_orig(sort(UniqueMomIdx),:);
+elseif strcmp(mom_method, 'GMM')
+ % for GMM we use the transformed matched_moments structure
+ matched_moments = matched_moments(sort(UniqueMomIdx),:);
+end
\ No newline at end of file
diff --git a/matlab/+mom/mode_compute_gmm_smm.m b/matlab/+mom/mode_compute_gmm_smm.m
new file mode 100644
index 0000000000000000000000000000000000000000..4b934bc512c1f6595d3e72b7e490638c8c1de002
--- /dev/null
+++ b/matlab/+mom/mode_compute_gmm_smm.m
@@ -0,0 +1,129 @@
+function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds)
+% function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds)
+% -------------------------------------------------------------------------
+% Iterated method of moments for GMM and SMM, computes the minimum of the
+% objective function (distance between data moments and model moments)
+% for a sequence of optimizers and GMM/SMM iterations with different
+% weighting matrices.
+% =========================================================================
+% INPUTS
+% xparam0: [vector] vector of initialized parameters
+% objective_function: [func handle] name of the objective function
+% oo_: [structure] results
+% M_: [structure] model information
+% options_mom_: [structure] options
+% estim_params_: [structure] information on estimated parameters
+% bayestopt_: [structure] information on priors
+% Bounds: [structure] bounds for optimization
+% -------------------------------------------------------------------------
+% OUTPUT
+% xparam1: [vector] mode of objective function
+% oo_: [structure] updated results
+% Woptflag: [logical] true if optimal weighting matrix was computed
+% -------------------------------------------------------------------------
+% This function is called by
+% o mom.run
+% -------------------------------------------------------------------------
+% This function calls
+% o mom.optimal_weighting_matrix
+% o mom.display_estimation_results_table
+% o dynare_minimize_objective
+% o mom.objective_function
+% =========================================================================
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+if size(options_mom_.mom.weighting_matrix,1)>1 && ~(any(strcmpi('diagonal',options_mom_.mom.weighting_matrix)) || any(strcmpi('optimal',options_mom_.mom.weighting_matrix)))
+ fprintf('\nYou did not specify the use of an optimal or diagonal weighting matrix. There is no point in running an iterated method of moments.\n')
+end
+
+for stage_iter = 1:size(options_mom_.mom.weighting_matrix,1)
+ fprintf('Estimation stage %u\n',stage_iter);
+ Woptflag = false;
+ switch lower(options_mom_.mom.weighting_matrix{stage_iter})
+ case 'identity_matrix'
+ fprintf(' - identity weighting matrix\n');
+ weighting_matrix = eye(options_mom_.mom.mom_nbr);
+ case 'diagonal'
+ fprintf(' - diagonal of optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
+ if stage_iter == 1
+ fprintf(' and using data-moments as initial estimate of model-moments\n');
+ weighting_matrix = diag(diag( mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag) ));
+ else
+ fprintf(' and using previous stage estimate of model-moments\n');
+ weighting_matrix = diag(diag( mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag) ));
+ end
+ case 'optimal'
+ fprintf(' - optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
+ if stage_iter == 1
+ fprintf(' and using data-moments as initial estimate of model-moments\n');
+ weighting_matrix = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag);
+ else
+ fprintf(' and using previous stage estimate of model-moments\n');
+ weighting_matrix = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
+ Woptflag = true;
+ end
+ otherwise % user specified matrix in file
+ fprintf(' - user-specified weighting matrix\n');
+ try
+ load(options_mom_.mom.weighting_matrix{stage_iter},'weighting_matrix')
+ catch
+ error(['method_of_moments: No matrix named ''weighting_matrix'' could be found in ',options_mom_.mom.weighting_matrix{stage_iter},'.mat !'])
+ end
+ [nrow, ncol] = size(weighting_matrix);
+ if ~isequal(nrow,ncol) || ~isequal(nrow,length(oo_.mom.data_moments)) %check if square and right size
+ error(['method_of_moments: ''weighting_matrix'' must be square and have ',num2str(length(oo_.mom.data_moments)),' rows and columns!'])
+ end
+ end
+ try % check for positive definiteness of weighting_matrix
+ oo_.mom.Sw = chol(weighting_matrix);
+ catch
+ error('method_of_moments: Specified ''weighting_matrix'' is not positive definite. Check whether your model implies stochastic singularity!')
+ end
+
+ for optim_iter = 1:length(options_mom_.optimizer_vec)
+ options_mom_.current_optimizer = options_mom_.optimizer_vec{optim_iter};
+ if options_mom_.optimizer_vec{optim_iter} == 0
+ xparam1 = xparam0; % no minimization, evaluate objective at current values
+ fval = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_);
+ else
+ if options_mom_.optimizer_vec{optim_iter} == 13
+ options_mom_.mom.vector_output = true;
+ else
+ options_mom_.mom.vector_output = false;
+ end
+ if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_jacobian && ismember(options_mom_.optimizer_vec{optim_iter},options_mom_.mom.analytic_jacobian_optimizers) %do this only for gradient-based optimizers
+ options_mom_.mom.compute_derivs = true;
+ else
+ options_mom_.mom.compute_derivs = false;
+ end
+ [xparam1, fval, exitflag] = dynare_minimize_objective(objective_function, xparam0, options_mom_.optimizer_vec{optim_iter}, options_mom_, [Bounds.lb Bounds.ub], bayestopt_.name, bayestopt_, [],...
+ Bounds, oo_, estim_params_, M_, options_mom_);
+ if options_mom_.mom.vector_output
+ fval = fval'*fval;
+ end
+ end
+ fprintf('\nStage %d Iteration %d: Value of minimized moment distance objective function: %12.10f.\n',stage_iter,optim_iter,fval)
+ if options_mom_.mom.verbose
+ oo_.mom = display_estimation_results_table(xparam1,NaN(size(xparam1)),M_,options_mom_,estim_params_,bayestopt_,oo_.mom,prior_dist_names,sprintf('%s (STAGE %d ITERATION %d) VERBOSE',options_mom_.mom.mom_method,stage_iter,optim_iter),sprintf('verbose_%s_stage_%d_iter_%d',lower(options_mom_.mom.mom_method),stage_iter,optim_iter));
+ end
+ xparam0 = xparam1;
+ end
+ options_mom_.vector_output = false;
+ [~, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_); % get oo_.mom.model_moments for iterated GMM/SMM to compute optimal weighting matrix
+end
\ No newline at end of file
diff --git a/matlab/+mom/objective_function.m b/matlab/+mom/objective_function.m
index 13a301d7d1f33699a94377850fabce0251f71070..419c7112b249361f8d276a625f4db0035cf036c6 100644
--- a/matlab/+mom/objective_function.m
+++ b/matlab/+mom/objective_function.m
@@ -1,42 +1,43 @@
-function [fval, info, exit_flag, df, junk1, oo_, M_, options_mom_] = objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
-% [fval, info, exit_flag, df, junk1, oo_, M_, options_mom_] = objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
+function [fval, info, exit_flag, df, junkHessian, oo_, M_] = objective_function(xparam, Bounds, oo_, estim_params_, M_, options_mom_, bayestopt_)
+% [fval, info, exit_flag, df, junk1, oo_, M_] = objective_function(xparam, Bounds, oo_, estim_params_, M_, options_mom_, bayestopt_)
% -------------------------------------------------------------------------
-% This function evaluates the objective function for GMM/SMM estimation
+% This function evaluates the objective function for method of moments estimation
% =========================================================================
% INPUTS
-% o xparam1: current value of estimated parameters as returned by set_prior()
-% o Bounds: structure containing parameter bounds
-% o oo_: structure for results
-% o estim_params_: structure describing the estimated_parameters
-% o M_ structure describing the model
-% o options_mom_: structure information about all settings (specified by the user, preprocessor, and taken from global options_)
+% o xparam: [vector] current value of estimated parameters as returned by set_prior()
+% o Bounds: [structure] containing parameter bounds
+% o oo_: [structure] for results
+% o estim_params_: [structure] describing the estimated_parameters
+% o M_ [structure] describing the model
+% o options_mom_: [structure] information about all settings (specified by the user, preprocessor, and taken from global options_)
+% o bayestopt_: [structure] information about the prior
% -------------------------------------------------------------------------
% OUTPUTS
-% o fval: value of the quadratic form of the moment difference (except for lsqnonlin, where this is done implicitly)
-% o info: vector storing error code and penalty
-% o exit_flag: 0 if error, 1 if no error
-% o df: analytical parameter Jacobian of the quadratic form of the moment difference (for GMM only)
-% o junk1: empty matrix required for optimizer interface (Hessian would go here)
-% o oo_: structure containing the results with the following updated fields:
-% - mom.model_moments [numMom x 1] vector with model moments
-% - mom.Q value of the quadratic form of the moment difference
-% - mom.model_moments_params_derivs
-% [numMom x numParams] Jacobian matrix of derivatives of model_moments with respect to estimated parameters
-% (only for GMM with analytical derivatives)
-% o M_: Matlab's structure describing the model
-% o options_mom_: structure information about all settings (specified by the user, preprocessor, and taken from global options_)
+% o fval: [double] value of the quadratic form of the moment difference (except for lsqnonlin, where this is done implicitly)
+% o info: [vector] information on error codes and penalties
+% o exit_flag: [double] flag for exit status (0 if error, 1 if no error)
+% o df: [matrix] analytical jacobian of the moment difference (wrt paramters), currently for GMM only
+% o junkHessian: [matrix] empty matrix required for optimizer interface (Hessian would typically go here)
+% o oo_: [structure] results with the following updated fields:
+% - oo_.mom.model_moments: [vector] model moments
+% - oo_.mom.Q: [double] value of the quadratic form of the moment difference
+% - oo_.mom.model_moments_params_derivs: [matrix] analytical jacobian of the model moments wrt estimated parameters (currently for GMM only)
+% o M_: [structure] updated model structure
% -------------------------------------------------------------------------
% This function is called by
-% o mom.run.m
-% o dynare_minimize_objective.m
+% o mom.run
+% o dynare_minimize_objective
% -------------------------------------------------------------------------
% This function calls
-% o check_bounds_and_definiteness_estimation
-% o pruned_state_space_system
-% o resol
-% o set_all_parameters
+% o check_bounds_and_definiteness_estimation
+% o get_perturbation_params_derivs
+% o mom.get_data_moments
+% o pruned_state_space_system
+% o resol
+% o set_all_parameters
+% o simult_
% =========================================================================
-% Copyright © 2020-2021 Dynare Team
+% Copyright © 2020-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -52,65 +53,65 @@ function [fval, info, exit_flag, df, junk1, oo_, M_, options_mom_] = objective_f
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-% -------------------------------------------------------------------------
-% Author(s):
-% o Willi Mutschler (willi@mutschler.eu)
-% o Johannes Pfeifer (jpfeifer@uni-koeln.de)
% =========================================================================
+%% TO DO
+% check the info values and make use of meaningful penalties
+% how do we do the penalty for the prior??
+
+
%------------------------------------------------------------------------------
-% 0. Initialization of the returned variables and others...
+% Initialization of the returned variables and others...
%------------------------------------------------------------------------------
-if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
- if options_mom_.vector_output == 1
- if options_mom_.mom.penalized_estimator
- df = nan(size(oo_.mom.data_moments,1)+length(xparam1),length(xparam1));
+junkHessian = [];
+df = []; % required to be empty by e.g. newrat
+if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
+ if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
+ if options_mom_.mom.vector_output == 1
+ if options_mom_.mom.penalized_estimator
+ df = nan(size(oo_.mom.data_moments,1)+length(xparam),length(xparam));
+ else
+ df = nan(size(oo_.mom.data_moments,1),length(xparam));
+ end
else
- df = nan(size(oo_.mom.data_moments,1),length(xparam1));
+ df = nan(1,length(xparam));
end
- else
- df = nan(1,length(xparam1));
end
-else
- df=[]; %required to be empty by e.g. newrat
end
-junk1 = [];
-junk2 = [];
+
%--------------------------------------------------------------------------
-% 1. Get the structural parameters & define penalties
+% Get the structural parameters and define penalties
%--------------------------------------------------------------------------
% Ensure that xparam1 is a column vector; particleswarm.m requires this.
-xparam1 = xparam1(:);
-
-M_ = set_all_parameters(xparam1, estim_params_, M_);
-
-[fval,info,exit_flag]=check_bounds_and_definiteness_estimation(xparam1, M_, estim_params_, Bounds);
+xparam = xparam(:);
+M_ = set_all_parameters(xparam, estim_params_, M_);
+[fval,info,exit_flag] = check_bounds_and_definiteness_estimation(xparam, M_, estim_params_, Bounds);
if info(1)
- if options_mom_.vector_output == 1 % lsqnonlin requires vector output
+ if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
end
return
end
+
%--------------------------------------------------------------------------
-% 2. call resol to compute steady state and model solution
+% Call resol to compute steady state and model solution
%--------------------------------------------------------------------------
% Compute linear approximation around the deterministic steady state
[dr, info, M_, oo_] = resol(0, M_, options_mom_, oo_);
-
% Return, with endogenous penalty when possible, if resol issues an error code
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
info(1) == 81 || info(1) == 84 || info(1) == 85 || info(1) == 86
- %meaningful second entry of output that can be used
+ % meaningful second entry of output that can be used
fval = Inf;
info(4) = info(2);
exit_flag = 0;
- if options_mom_.vector_output == 1 % lsqnonlin requires vector output
+ if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
end
return
@@ -118,31 +119,32 @@ if info(1)
fval = Inf;
info(4) = 0.1;
exit_flag = 0;
- if options_mom_.vector_output == 1 % lsqnonlin requires vector output
+ if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
end
return
end
end
+
+%--------------------------------------------------------------------------
+% GMM: Set up pruned state-space system and compute model moments
+%--------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'GMM')
- %--------------------------------------------------------------------------
- % 3. Set up pruned state-space system and compute model moments
- %--------------------------------------------------------------------------
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
- indpmodel = []; %initialize index for model parameters
+ indpmodel = []; % initialize index for model parameters
if ~isempty(estim_params_.param_vals)
- indpmodel = estim_params_.param_vals(:,1); %values correspond to parameters declaration order, row number corresponds to order in estimated_params
+ indpmodel = estim_params_.param_vals(:,1); % values correspond to parameters declaration order, row number corresponds to order in estimated_params
end
- indpstderr=[]; %initialize index for stderr parameters
+ indpstderr=[]; % initialize index for stderr parameters
if ~isempty(estim_params_.var_exo)
- indpstderr = estim_params_.var_exo(:,1); %values correspond to varexo declaration order, row number corresponds to order in estimated_params
+ indpstderr = estim_params_.var_exo(:,1); % values correspond to varexo declaration order, row number corresponds to order in estimated_params
end
- indpcorr=[]; %initialize matrix for corr paramters
+ indpcorr=[]; % initialize matrix for corr paramters
if ~isempty(estim_params_.corrx)
- indpcorr = estim_params_.corrx(:,1:2); %values correspond to varexo declaration order, row number corresponds to order in estimated_params
+ indpcorr = estim_params_.corrx(:,1:2); % values correspond to varexo declaration order, row number corresponds to order in estimated_params
end
- if estim_params_.nvn || estim_params_.ncn %nvn is number of stderr parameters and ncn is number of corr parameters of measurement innovations as declared in estimated_params
+ if estim_params_.nvn || estim_params_.ncn % nvn is number of stderr parameters and ncn is number of corr parameters of measurement innovations as declared in estimated_params
error('Analytic computation of standard errrors does not (yet) support measurement errors.\nInstead, define them explicitly as varexo and provide measurement equations in the model definition.\nAlternatively, use numerical standard errors.')
end
modparam_nbr = estim_params_.np; % number of model parameters as declared in estimated_params
@@ -151,27 +153,26 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
totparam_nbr = stderrparam_nbr+corrparam_nbr+modparam_nbr;
dr.derivs = get_perturbation_params_derivs(M_, options_mom_, estim_params_, oo_, indpmodel, indpstderr, indpcorr, 0); %analytic derivatives of perturbation matrices
oo_.mom.model_moments_params_derivs = NaN(options_mom_.mom.mom_nbr,totparam_nbr);
- pruned_state_space = pruned_state_space_system(M_, options_mom_, dr, oo_.dr.obs_var, options_mom_.ar, 0, 1);
+ pruned_state_space = pruned_state_space_system(M_, options_mom_, dr, oo_.mom.obs_var, options_mom_.ar, 0, 1);
else
- pruned_state_space = pruned_state_space_system(M_, options_mom_, dr, oo_.dr.obs_var, options_mom_.ar, 0, 0);
+ pruned_state_space = pruned_state_space_system(M_, options_mom_, dr, oo_.mom.obs_var, options_mom_.ar, 0, 0);
end
-
oo_.mom.model_moments = NaN(options_mom_.mom.mom_nbr,1);
for jm = 1:size(M_.matched_moments,1)
% First moments
if ~options_mom_.prefilter && (sum(M_.matched_moments{jm,3}) == 1)
- idx1 = (oo_.dr.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}) );
+ idx1 = (oo_.mom.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}) );
oo_.mom.model_moments(jm,1) = pruned_state_space.E_y(idx1);
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
oo_.mom.model_moments_params_derivs(jm,:) = pruned_state_space.dE_y(idx1,:);
end
end
- % Second moments
+ % second moments
if (sum(M_.matched_moments{jm,3}) == 2)
- idx1 = (oo_.dr.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}(1)) );
- idx2 = (oo_.dr.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}(2)) );
+ idx1 = (oo_.mom.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}(1)) );
+ idx2 = (oo_.mom.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}(2)) );
if nnz(M_.matched_moments{jm,2}) == 0
- % Covariance
+ % covariance
if options_mom_.prefilter
oo_.mom.model_moments(jm,1) = pruned_state_space.Var_y(idx1,idx2);
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
@@ -186,8 +187,8 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
end
end
else
- % Autocovariance
- lag = -M_.matched_moments{jm,2}(2); %note that leads/lags in matched_moments are transformed such that first entry is always 0 and the second is a lag
+ % autocovariance
+ lag = -M_.matched_moments{jm,2}(2); %note that leads/lags in M_.matched_moments are transformed such that first entry is always 0 and the second is a lag
if options_mom_.prefilter
oo_.mom.model_moments(jm,1) = pruned_state_space.Var_yi(idx1,idx2,lag);
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
@@ -204,24 +205,23 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
end
end
end
+end
-elseif strcmp(options_mom_.mom.mom_method,'SMM')
- %------------------------------------------------------------------------------
- % 3. Compute Moments of the model solution for normal innovations
- %------------------------------------------------------------------------------
-
+%------------------------------------------------------------------------------
+% SMM: Compute Moments of the model solution for Gaussian innovations
+%------------------------------------------------------------------------------
+if strcmp(options_mom_.mom.mom_method,'SMM')
% create shock series with correct covariance matrix from iid standard normal shocks
- i_exo_var = setdiff(1:M_.exo_nbr, find(diag(M_.Sigma_e) == 0 )); %find singular entries in covariance
+ i_exo_var = setdiff(1:M_.exo_nbr, find(diag(M_.Sigma_e) == 0 )); % find singular entries in covariance
chol_S = chol(M_.Sigma_e(i_exo_var,i_exo_var));
- scaled_shock_series = zeros(size(options_mom_.mom.shock_series)); %initialize
- scaled_shock_series(:,i_exo_var) = options_mom_.mom.shock_series(:,i_exo_var)*chol_S; %set non-zero entries
-
+ scaled_shock_series = zeros(size(options_mom_.mom.shock_series)); % initialize
+ scaled_shock_series(:,i_exo_var) = options_mom_.mom.shock_series(:,i_exo_var)*chol_S; % set non-zero entries
% simulate series
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_.vector_output == 1 % lsqnonlin requires vector output
+ if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = Inf(size(oo_.mom.Sw,1),1);
else
fval = Inf;
@@ -229,73 +229,70 @@ elseif strcmp(options_mom_.mom.mom_method,'SMM')
info(1)=180;
info(4) = 0.1;
exit_flag = 0;
- if options_mom_.vector_output == 1 % lsqnonlin requires vector output
+ if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
end
return
end
-
- % Remove burn-in and focus on observables (note that y_sim is in declaration order)
- y_sim = y_sim(oo_.dr.order_var(oo_.dr.obs_var) , end-options_mom_.mom.long+1:end)';
-
+ % remove burn-in and focus on observables (note that y_sim is in declaration order)
+ y_sim = y_sim(oo_.dr.order_var(oo_.mom.obs_var) , end-options_mom_.mom.long+1:end)';
if ~all(diag(M_.H)==0)
i_ME = setdiff([1:size(M_.H,1)],find(diag(M_.H) == 0)); % find ME with 0 variance
- chol_S = chol(M_.H(i_ME,i_ME)); %decompose rest
- shock_mat=zeros(size(options_mom_.mom.ME_shock_series)); %initialize
+ chol_S = chol(M_.H(i_ME,i_ME)); % decompose rest
+ shock_mat=zeros(size(options_mom_.mom.ME_shock_series)); % initialize
shock_mat(:,i_ME)=options_mom_.mom.ME_shock_series(:,i_ME)*chol_S;
y_sim = y_sim+shock_mat;
end
-
- % Remove mean if centered moments
+ % remove mean if centered moments
if options_mom_.prefilter
y_sim = bsxfun(@minus, y_sim, mean(y_sim,1));
end
- oo_.mom.model_moments = mom.data_moments(y_sim, oo_, M_.matched_moments, options_mom_);
-
+ oo_.mom.model_moments = mom.get_data_moments(y_sim, oo_, M_.matched_moments, options_mom_);
end
+
%--------------------------------------------------------------------------
-% 4. Compute quadratic target function
+% Compute quadratic target function
%--------------------------------------------------------------------------
moments_difference = oo_.mom.data_moments - oo_.mom.model_moments;
-residuals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*oo_.mom.Sw*moments_difference;
-oo_.mom.Q = residuals'*residuals;
-if options_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))];
- 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);
- end
-end
-if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
- if options_mom_.mom.penalized_estimator
- dxparam1 = eye(length(xparam1));
+if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
+ residuals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*oo_.mom.Sw*moments_difference;
+ 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;(xparam-oo_.mom.prior.mean)./sqrt(diag(oo_.mom.prior.variance))];
+ end
+ else
+ fval = oo_.mom.Q;
+ if options_mom_.mom.penalized_estimator
+ fval=fval+(xparam-oo_.mom.prior.mean)'/oo_.mom.prior.variance*(xparam-oo_.mom.prior.mean);
+ end
end
-
- for jp=1:length(xparam1)
- dmoments_difference = - oo_.mom.model_moments_params_derivs(:,jp);
- dresiduals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*oo_.mom.Sw*dmoments_difference;
-
- if options_mom_.vector_output == 1 % lsqnonlin requires vector output
- if options_mom_.mom.penalized_estimator
- df(:,jp)=[dresiduals;dxparam1(:,jp)./sqrt(diag(oo_.prior.variance))];
+ if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
+ if options_mom_.mom.penalized_estimator
+ dxparam1 = eye(length(xparam));
+ end
+ for jp=1:length(xparam)
+ dmoments_difference = - oo_.mom.model_moments_params_derivs(:,jp);
+ dresiduals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*oo_.mom.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(oo_.mom.prior.variance))];
+ else
+ df(:,jp) = dresiduals;
+ end
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) = dresiduals'*residuals + residuals'*dresiduals;
+ if options_mom_.mom.penalized_estimator
+ 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));
+ end
end
end
end
end
-end%main function end
+end % main function end
diff --git a/matlab/+mom/print_info_on_estimation_settings.m b/matlab/+mom/print_info_on_estimation_settings.m
new file mode 100644
index 0000000000000000000000000000000000000000..45dfe832a551db8189f5b3fe7082b742a5a6e57d
--- /dev/null
+++ b/matlab/+mom/print_info_on_estimation_settings.m
@@ -0,0 +1,123 @@
+function print_info_on_estimation_settings(options_mom_, number_of_estimated_parameters)
+% function print_info_on_estimation_settings(options_mom_, number_of_estimated_parameters)
+% -------------------------------------------------------------------------
+% Print information on the method of moments estimation settings to the console
+% =========================================================================
+% INPUTS
+% options_mom_ [struct] Options for the method of moments estimation
+% number_of_estimated_parameters [integer] Number of estimated parameters
+% -------------------------------------------------------------------------
+% OUTPUT
+% No output, just displays the chosen settings
+% -------------------------------------------------------------------------
+% This function is called by
+% o mom.run
+% -------------------------------------------------------------------------
+% This function calls
+% o skipline
+% =========================================================================
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+fprintf('\n---------------------------------------------------\n')
+if strcmp(options_mom_.mom.mom_method,'SMM')
+ fprintf('Simulated method of moments with');
+elseif strcmp(options_mom_.mom.mom_method,'GMM')
+ fprintf('General method of moments with');
+end
+if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_method,'GMM')
+ if options_mom_.prefilter
+ fprintf('\n - centered moments (prefilter=1)');
+ else
+ fprintf('\n - uncentered moments (prefilter=0)');
+ end
+ if options_mom_.mom.penalized_estimator
+ fprintf('\n - penalized estimation using deviation from prior mean and weighted with prior precision');
+ end
+end
+
+for i = 1:length(options_mom_.optimizer_vec)
+ if i == 1
+ str = '- optimizer (mode_compute';
+ else
+ str = ' (additional_optimizer_steps';
+ end
+ switch options_mom_.optimizer_vec{i}
+ case 0
+ fprintf('\n %s=0): no minimization',str);
+ case 1
+ fprintf('\n %s=1): fmincon',str);
+ case 2
+ fprintf('\n %s=2): continuous simulated annealing',str);
+ case 3
+ fprintf('\n %s=3): fminunc',str);
+ case 4
+ fprintf('\n %s=4): csminwel',str);
+ case 5
+ fprintf('\n %s=5): newrat',str);
+ case 6
+ fprintf('\n %s=6): gmhmaxlik',str);
+ case 7
+ fprintf('\n %s=7): fminsearch',str);
+ case 8
+ fprintf('\n %s=8): Dynare Nelder-Mead simplex',str);
+ case 9
+ fprintf('\n %s=9): CMA-ES',str);
+ case 10
+ fprintf('\n %s=10): simpsa',str);
+ case 11
+ skipline;
+ error('method_of_moments: online_auxiliary_filter (mode_compute=11) is only supported with likelihood-based estimation techniques!');
+ case 12
+ fprintf('\n %s=12): particleswarm',str);
+ case 101
+ fprintf('\n %s=101): SolveOpt',str);
+ case 102
+ fprintf('\n %s=102): simulannealbnd',str);
+ case 13
+ fprintf('\n %s=13): lsqnonlin',str);
+ otherwise
+ if ischar(options_mom_.optimizer_vec{i})
+ fprintf('\n %s=%s): user-defined',str,options_mom_.optimizer_vec{i});
+ else
+ skipline;
+ error('method_of_moments: Unknown optimizer!');
+ end
+ end
+ if options_mom_.silent_optimizer
+ fprintf(' (silent)');
+ end
+ if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_jacobian && ismember(options_mom_.optimizer_vec{i},options_mom_.mom.analytic_jacobian_optimizers)
+ fprintf(' (using analytical Jacobian)');
+ end
+end
+if options_mom_.order > 0
+ fprintf('\n - stochastic simulations with perturbation order: %d', options_mom_.order)
+end
+if options_mom_.order > 1 && options_mom_.pruning
+ fprintf(' (with pruning)')
+end
+if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
+ if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors
+ fprintf('\n - standard errors: analytic derivatives');
+ else
+ fprintf('\n - standard errors: numerical derivatives');
+ end
+ fprintf('\n - number of matched moments: %d', options_mom_.mom.mom_nbr);
+end
+fprintf('\n - number of parameters: %d', number_of_estimated_parameters);
+fprintf('\n\n');
\ No newline at end of file
diff --git a/matlab/+mom/run.m b/matlab/+mom/run.m
index 720006edbbb9ebc9c7de4238b178e15cf1cbf334..1a57a9be47f8f036e7154643d511c111d9617dd9 100644
--- a/matlab/+mom/run.m
+++ b/matlab/+mom/run.m
@@ -1,68 +1,84 @@
function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_, M_, options_mom_)
-%function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_, M_, options_mom_)
+% function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_, M_, options_mom_)
% -------------------------------------------------------------------------
% This function performs a method of moments estimation with the following steps:
-% Step 0: Check if required structures and options exist
-% Step 1: - Prepare options_mom_ structure
-% - Carry over options from the preprocessor
-% - Initialize other options
-% - Get variable orderings and state space representation
-% Step 2: Checks and transformations for matched moments structure
-% Step 3: Checks and transformations for estimated parameters, priors, and bounds
-% Step 4: Checks and transformations for data
-% Step 5: Checks for steady state at initial parameters
-% Step 6: Checks for objective function at initial parameters
-% Step 7: Iterated method of moments estimation
-% Step 8: J-Test
-% Step 9: Clean up
+% o Checking if required structures and options exist
+% o Preparing local options_mom_ structure
+% o Checking the options and the compatibility of the settings
+% o Initializations of variables, orderings and state space representation
+% o Checks and transformations for matched moments structure
+% o Checks and transformations for estimated parameters, priors, and bounds
+% o Checks and transformations for data
+% o Checks for objective function at initial parameters
+% o GMM/SMM: iterated method of moments estimation
+% o GMM/SMM: J-Test and fit of moments%
+% o Display of results
+% o Clean up
% -------------------------------------------------------------------------
% This function is inspired by replication codes accompanied to the following papers:
+% GMM/SMM:
% o Andreasen, Fernández-Villaverde, Rubio-Ramírez (2018): "The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications", Review of Economic Studies, 85(1):1-49.
% o Born, Pfeifer (2014): "Risk Matters: Comment", American Economic Review, 104(12):4231-4239.
% o Mutschler (2018): "Higher-order statistics for DSGE models", Econometrics and Statistics, 6:44-56.
% =========================================================================
% INPUTS
-% o bayestopt_: [structure] information about priors
-% o options_: [structure] information about global options
-% o oo_: [structure] storage for results
-% o estim_params_: [structure] information about estimated parameters
-% o M_: [structure] information about model with
-% o matched_moments: [cell] information about selected moments to match in estimation
-% vars: matched_moments{:,1});
-% lead/lags: matched_moments{:,2};
-% powers: matched_moments{:,3};
-% o options_mom_: [structure] information about settings specified by the user
+% o bayestopt_: [structure] information about priors
+% o options_: [structure] information about global options
+% o oo_: [structure] storage for results
+% o estim_params_: [structure] information about estimated parameters
+% o M_: [structure] information about model with
+% o matched_moments: [cell] information about selected moments to match in GMM/SMM estimation
+% vars: matched_moments{:,1});
+% lead/lags: matched_moments{:,2};
+% powers: matched_moments{:,3};
+% o options_mom_: [structure] information about settings specified by the user
% -------------------------------------------------------------------------
% OUTPUTS
-% o oo_: [structure] storage for results (oo_)
-% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
+% o oo_: [structure] storage for results (oo_)
+% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
+% o M_: [structure] updated information about model
% -------------------------------------------------------------------------
% This function is called by
% o driver.m
% -------------------------------------------------------------------------
% This function calls
-% o check_for_calibrated_covariances.m
-% o check_prior_bounds.m
-% o do_parameter_initialization.m
-% o dynare_minimize_objective.m
-% o evaluate_steady_state
-% o get_all_parameters.m
-% o get_matrix_entries_for_psd_check.m
-% o makedataset.m
-% o mom.check_plot.m
-% o mom.data_moments.m
-% o mom.objective_function.m
+% o cellofchararraymaxlength
+% o check_for_calibrated_covariances
+% o check_prior_bounds
+% o check_prior_stderr_corr
+% o check_steady_state_changes_parameters
+% o check_varobs_are_endo_and_declared_once
+% o display_estimation_results_table
+% o do_parameter_initialization
+% o dyn_latex_table
+% o dynare_minimize_objective
+% o dyntable
+% o get_all_parameters
+% o get_dynare_random_generator_state
+% o get_matrix_entries_for_psd_check
+% o M_.fname '_prior_restrictions'
+% o makedataset
+% o mom.check_plot
+% o mom.default_option_mom_values
+% o mom.get_data_moments
+% o mom.matched_moments_block
+% o mom.objective_function
% o mom.optimal_weighting_matrix
-% o mom-standard_errors
-% o plot_priors.m
-% o print_info.m
-% o prior_bounds.m
-% o set_default_option.m
-% o set_prior.m
-% o set_state_space.m
-% o set_all_parameters.m
-% o test_for_deep_parameters_calibration.m
-
+% o mom.print_info_on_estimation_settings
+% o mom.set_correct_bounds_for_stderr_corr
+% o mom.standard_errors
+% o plot_priors
+% o prior_bounds
+% o priordens
+% o print_info
+% o set_all_parameters
+% o set_dynare_random_generator_state
+% o set_prior
+% o set_state_space
+% o skipline
+% o test_for_deep_parameters_calibration
+% o warning_config
+% =========================================================================
% Copyright © 2020-2023 Dynare Team
%
% This file is part of Dynare.
@@ -80,573 +96,355 @@ function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_,
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% -------------------------------------------------------------------------
-% Author(s):
+% Maintaining Author(s):
% o Willi Mutschler (willi@mutschler.eu)
-% o Johannes Pfeifer (jpfeifer@uni-koeln.de)
+% o Johannes Pfeifer (johannes.pfeifer@unibw.de)
% =========================================================================
-%% TO DO LIST
-% - [ ] add IRF matching
-% - [ ] speed up pruned_state_space_system (by using doubling with old initial values, hardcoding zeros, other "tricks" used in e.g. nlma)
-% - [ ] add option to use autocorrelations (we have useautocorr in identification toolbox already)
-% - [ ] SMM with extended path
-% - [ ] deal with measurement errors (once @wmutschl has implemented this in identification toolbox)
-% - [ ] dirname option to save output to different directory not yet implemented
-% - [ ] display scaled moments
+% -------------------------------------------------------------------------
+% TO DO LISTS
+% -------------------------------------------------------------------------
+% GENERAL
+% - document all options in manual
+% - document analytic_jacobian better
+% - make endogenous_prior_restrictions work
+% - dirname option to save output to different directory not yet implemented
+% - create test for prior restrictions file
+% - add mode_file option
+% - implement penalty objective
+% - test optimizers
+% GMM/SMM
+% - speed up pruned_state_space_system (by using doubling with old initial values, hardcoding zeros, other "tricks" used in e.g. nlma)
+% - add option to use autocorrelations (we have useautocorr in identification toolbox already)
+% - SMM with extended path
+% - deal with measurement errors (once @wmutschl has implemented this in identification toolbox)
+% - display scaled moments
+% - enable first moments despite prefilter
+% - do "true" Bayesian GMM and SMM not only penalized
+
+fprintf('\n==== Method of Moments Estimation (%s) ====\n\n',options_mom_.mom.mom_method)
-% The TeX option crashes MATLAB R2014a run with "-nodisplay" option
-% (as is done from the testsuite).
-% Since we can’t directly test whether "-nodisplay" has been passed,
-% we test for the "TOP_TEST_DIR" environment variable, which is set
-% by the testsuite.
-% Note that it was not tested whether the crash happens with more
-% recent MATLAB versions, so when OLD_MATLAB_VERSION is increased,
-% one should make a test before removing this workaround.
-if options_.TeX && ~isoctave && matlab_ver_less_than('8.4') && ~isempty(getenv('TOP_TEST_DIR'))
- warning('Disabling TeX option due to a bug in MATLAB R2014a with -nodisplay')
- options_.TeX = false;
-end
-if isfield(options_mom_, 'TeX') && options_mom_.TeX && ~isoctave && matlab_ver_less_than('8.4') && ~isempty(getenv('TOP_TEST_DIR'))
- warning('Disabling TeX option due to a bug in MATLAB R2014a with -nodisplay')
- options_mom_.TeX = false;
-end
% -------------------------------------------------------------------------
-% Step 0: Check if required structures and options exist
+% checks if required structures exist
% -------------------------------------------------------------------------
if isempty(estim_params_) % structure storing the info about estimated parameters in the estimated_params block
if ~(isfield(estim_params_,'nvx') && (size(estim_params_.var_exo,1)+size(estim_params_.var_endo,1)+size(estim_params_.corrx,1)+size(estim_params_.corrn,1)+size(estim_params_.param_vals,1))==0)
- error('method_of_moments: You need to provide an ''estimated_params'' block')
+ error('method_of_moments: You need to provide an ''estimated_params'' block!')
else
- error('method_of_moments: The ''estimated_params'' block must not be empty')
+ error('method_of_moments: The ''estimated_params'' block must not be empty!')
end
end
-if ~isfield(M_,'matched_moments') || isempty(M_.matched_moments) % structure storing the moments used for the method of moments estimation
- error('method_of_moments: You need to provide a ''matched_moments'' block')
+if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
+ if ~isfield(M_,'matched_moments') || isempty(M_.matched_moments) % structure storing the moments used for GMM and SMM estimation
+ error('method_of_moments: You need to provide a ''matched_moments'' block for ''mom_method=%s''!',options_mom_.mom.mom_method)
+ end
end
-if ~isempty(bayestopt_) && any(bayestopt_.pshape==0) && any(bayestopt_.pshape~=0)
- error('method_of_moments: Estimation must be either fully classical or fully Bayesian. Maybe you forgot to specify a prior distribution.')
+if (~isempty(estim_params_.var_endo) || ~isempty(estim_params_.corrn)) && strcmp(options_mom_.mom.mom_method, 'GMM')
+ error('method_of_moments: GMM estimation does not support measurement error(s) yet. Please specify them as a structural shock!')
end
-
-if options_.logged_steady_state || options_.loglinear
- error('method_of_moments: The loglinear option is not supported. Please append the required logged variables as auxiliary equations.\n')
+doBayesianEstimation = [estim_params_.var_exo(:,5); estim_params_.var_endo(:,5); estim_params_.corrx(:,6); estim_params_.corrn(:,6); estim_params_.param_vals(:,5)];
+if all(doBayesianEstimation~=0)
+ doBayesianEstimation = true;
+elseif all(doBayesianEstimation==0)
+ doBayesianEstimation = false;
else
- options_mom_.logged_steady_state = 0;
- options_mom_.loglinear = false;
+ error('method_of_moments: Estimation must be either fully Frequentist or fully Bayesian. Maybe you forgot to specify a prior distribution!')
+end
+if ~isfield(options_,'varobs')
+ error('method_of_moments: VAROBS statement is missing!')
end
+check_varobs_are_endo_and_declared_once(options_.varobs,M_.endo_names);
-fprintf('\n==== Method of Moments Estimation (%s) ====\n\n',options_mom_.mom.mom_method)
% -------------------------------------------------------------------------
-% Step 1a: Prepare options_mom_ structure
+% options_mom_ structure
% -------------------------------------------------------------------------
-% options_mom_ is local and contains default and user-specified values for
+% options_mom_ is local and contains default and user-specified values for
% all settings needed for the method of moments estimation. Some options,
% though, are set by the preprocessor into options_ and we copy these over.
% The idea is to be independent of options_ and have full control of the
% estimation instead of possibly having to deal with options chosen somewhere
% else in the mod file.
+options_mom_ = mom.default_option_mom_values(options_mom_, options_, M_.dname, doBayesianEstimation);
-% Method of Moments estimation options that can be set by the user in the mod file, otherwise default values are provided
+
+% -------------------------------------------------------------------------
+% workarounds
+% -------------------------------------------------------------------------
+% The TeX option crashes MATLAB R2014a run with "-nodisplay" option
+% (as is done from the testsuite).
+% Since we can’t directly test whether "-nodisplay" has been passed,
+% we test for the "TOP_TEST_DIR" environment variable, which is set
+% by the testsuite.
+% Note that it was not tested whether the crash happens with more
+% recent MATLAB versions, so when OLD_MATLAB_VERSION is increased,
+% one should make a test before removing this workaround.
+if options_.TeX && ~isoctave && matlab_ver_less_than('8.4') && ~isempty(getenv('TOP_TEST_DIR'))
+ warning('Disabling TeX option due to a bug in MATLAB R2014a with -nodisplay')
+ options_.TeX = false;
+end
+if isfield(options_mom_, 'TeX') && options_mom_.TeX && ~isoctave && matlab_ver_less_than('8.4') && ~isempty(getenv('TOP_TEST_DIR'))
+ warning('Disabling TeX option due to a bug in MATLAB R2014a with -nodisplay')
+ options_mom_.TeX = false;
+end
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
- options_mom_.mom = set_default_option(options_mom_.mom,'bartlett_kernel_lag',20); % bandwith in optimal weighting matrix
- options_mom_.mom = set_default_option(options_mom_.mom,'penalized_estimator',false); % include deviation from prior mean as additional moment restriction and use prior precision as weight
- options_mom_.mom = set_default_option(options_mom_.mom,'verbose',false); % display and store intermediate estimation results
- options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix',{'DIAGONAL'; 'OPTIMAL'}); % weighting matrix in moments distance objective function at each iteration of estimation;
- % possible values are 'OPTIMAL', 'IDENTITY_MATRIX' ,'DIAGONAL' or a filename. Size of cell determines stages in iterated estimation.
- options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix_scaling_factor',1); % scaling of weighting matrix in objective function
- options_mom_.mom = set_default_option(options_mom_.mom,'se_tolx',1e-5); % step size for numerical computation of standard errors
- options_mom_ = set_default_option(options_mom_,'order',1); % order of Taylor approximation in perturbation
- options_mom_ = set_default_option(options_mom_,'pruning',false); % use pruned state space system at higher-order
- % Checks for perturbation order
- if options_mom_.order < 1
- error('method_of_moments: The order of the Taylor approximation cannot be 0!')
+% temporary workaround for https://git.dynare.org/Dynare/dseries/-/issues/51
+ if options_mom_.xls_sheet~=1
+ evalin('base','options_.xls_sheet=options_mom_.xls_sheet');
+ end
+ if ~isempty(options_mom_.xls_range)
+ evalin('base','options_.xls_range=options_mom_.xls_range');
end
end
+
+
+% -------------------------------------------------------------------------
+% checks on settings
+% -------------------------------------------------------------------------
+if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
+ if numel(options_mom_.nobs) > 1
+ error('method_of_moments: Recursive estimation and forecast for samples is not supported. Please set an integer as ''nobs''!');
+ end
+ if numel(options_mom_.first_obs) > 1
+ error('method_of_moments: Recursive estimation and forecast for samples is not supported. Please set an integer as ''first_obs''!');
+ end
+end
+if options_mom_.order < 1
+ error('method_of_moments: The order of the Taylor approximation cannot be 0!')
+end
+if options_mom_.order > 2
+ fprintf('Dynare will use ''k_order_solver'' as the order>2\n');
+ options_mom_.k_order_solver = true;
+end
if strcmp(options_mom_.mom.mom_method,'SMM')
- options_mom_.mom = set_default_option(options_mom_.mom,'burnin',500); % number of periods dropped at beginning of simulation
- options_mom_.mom = set_default_option(options_mom_.mom,'bounded_shock_support',false); % trim shocks in simulation to +- 2 stdev
- options_mom_.mom = set_default_option(options_mom_.mom,'seed',24051986); % seed used in simulations
- options_mom_.mom = set_default_option(options_mom_.mom,'simulation_multiple',7); % multiple of the data length used for simulation
if options_mom_.mom.simulation_multiple < 1
- fprintf('The simulation horizon is shorter than the data. Dynare resets the simulation_multiple to 5.\n')
+ fprintf('The simulation horizon is shorter than the data. Dynare resets the simulation_multiple to 7.\n')
options_mom_.mom.simulation_multiple = 7;
end
end
if strcmp(options_mom_.mom.mom_method,'GMM')
- % Check for pruning with GMM at higher order
+ % require pruning with GMM at higher order
if options_mom_.order > 1 && ~options_mom_.pruning
fprintf('GMM at higher order only works with pruning, so we set pruning option to 1.\n');
options_mom_.pruning = true;
end
if options_mom_.order > 3
- error('method_of_moments: perturbation orders higher than 3 are not implemented for GMM estimation, try using SMM.\n');
+ error('method_of_moments: Perturbation orders higher than 3 are not implemented for GMM estimation, try using SMM!');
end
end
-options_mom_.mom = set_default_option(options_mom_.mom,'analytic_standard_errors',false); % compute standard errors numerically (0) or analytically (1). Analytical derivatives are only available for GMM.
-options_mom_.mom = set_default_option(options_mom_.mom,'analytic_jacobian',false); % use analytic Jacobian in optimization, only available for GMM and gradient-based optimizers
-% initialize flag to compute derivs in objective function (needed for GMM with either analytic_standard_errors or analytic_jacobian )
-options_mom_.mom.compute_derivs = false;
-
-% General options that can be set by the user in the mod file, otherwise default values are provided
-options_mom_ = set_default_option(options_mom_,'dirname',M_.dname); % specify directory in which to store estimation output [not yet working]
-options_mom_ = set_default_option(options_mom_,'graph_format','eps'); % specify the file format(s) for graphs saved to disk
-options_mom_ = set_default_option(options_mom_,'nodisplay',false); % do not display the graphs, but still save them to disk
-options_mom_ = set_default_option(options_mom_,'nograph',false); % do not create graphs (which implies that they are not saved to the disk nor displayed)
-options_mom_ = set_default_option(options_mom_,'noprint',false); % do not print output to console
-options_mom_ = set_default_option(options_mom_,'plot_priors',true); % control plotting of priors
-options_mom_ = set_default_option(options_mom_,'prior_trunc',1e-10); % probability of extreme values of the prior density that is ignored when computing bounds for the parameters
-options_mom_ = set_default_option(options_mom_,'TeX',false); % print TeX tables and graphics
-options_mom_ = set_default_option(options_mom_,'verbosity',false); % print TeX tables and graphics
-
-% Data and model options that can be set by the user in the mod file, otherwise default values are provided
-options_mom_ = set_default_option(options_mom_,'first_obs',1); % number of first observation
-options_mom_ = set_default_option(options_mom_,'logdata',false); % if data is already in logs
-options_mom_ = set_default_option(options_mom_,'nobs',NaN); % number of observations
-options_mom_ = set_default_option(options_mom_,'prefilter',false); % demean each data series by its empirical mean and use centered moments
-options_mom_ = set_default_option(options_mom_,'xls_sheet',1); % name of sheet with data in Excel
-options_mom_ = set_default_option(options_mom_,'xls_range',''); % range of data in Excel sheet
-% temporary workaround for https://git.dynare.org/Dynare/dseries/-/issues/51
-if options_mom_.xls_sheet~=1
- evalin('base','options_.xls_sheet=options_mom_.xls_sheet');
-end
-if ~isempty(options_mom_.xls_range)
- evalin('base','options_.xls_range=options_mom_.xls_range');
+if options_mom_.mom.analytic_jacobian && ~strcmp(options_mom_.mom.mom_method,'GMM')
+ options_mom_.mom.analytic_jacobian = false;
+ fprintf('\n''analytic_jacobian'' option will be dismissed as it only works with GMM.\n');
end
-% Recursive estimation and forecast are not supported
-if numel(options_mom_.nobs)>1
- error('method_of_moments: Recursive estimation and forecast for samples is not supported. Please set an integer as ''nobs''.');
-end
-if numel(options_mom_.first_obs)>1
- error('method_of_moments: Recursive estimation and forecast for samples is not supported. Please set an integer as ''first_obs''.');
-end
-
-% Optimization options that can be set by the user in the mod file, otherwise default values are provided
-options_mom_ = set_default_option(options_mom_,'huge_number',1e7); % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
-if (isoctave && user_has_octave_forge_package('optim')) || (~isoctave && user_has_matlab_license('optimization_toolbox'))
- options_mom_ = set_default_option(options_mom_,'mode_compute',13); % specifies lsqnonlin as default optimizer for minimization of moments distance
-else
- options_mom_ = set_default_option(options_mom_,'mode_compute',4); % specifies csminwel as fallback default option for minimization of moments distance
-end
-options_mom_ = set_default_option(options_mom_,'additional_optimizer_steps',[]); % vector of additional mode-finders run after mode_compute
-options_mom_ = set_default_option(options_mom_,'optim_opt',[]); % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
-options_mom_ = set_default_option(options_mom_,'silent_optimizer',false); % run minimization of moments distance silently without displaying results or saving files in between
-% Check plot options that can be set by the user in the mod file, otherwise default values are provided
-options_mom_.mode_check.nolik = false; % we don't do likelihood (also this initializes mode_check substructure)
-options_mom_.mode_check = set_default_option(options_mom_.mode_check,'status',false); % plot the target function for values around the computed minimum for each estimated parameter in turn. This is helpful to diagnose problems with the optimizer.
-options_mom_.mode_check = set_default_option(options_mom_.mode_check,'neighbourhood_size',.5); % width of the window around the computed minimum to be displayed on the diagnostic plots. This width is expressed in percentage deviation. The Inf value is allowed, and will trigger a plot over the entire domain
-options_mom_.mode_check = set_default_option(options_mom_.mode_check,'symmetric_plots',true); % ensure that the check plots are symmetric around the minimum. A value of 0 allows to have asymmetric plots, which can be useful if the minimum is close to a domain boundary, or in conjunction with neighbourhood_size = Inf when the domain is not the entire real line
-options_mom_.mode_check = set_default_option(options_mom_.mode_check,'number_of_points',20); % number of points around the minimum where the target function is evaluated (for each parameter)
-
-% Numerical algorithms options that can be set by the user in the mod file, otherwise default values are provided
-options_mom_ = set_default_option(options_mom_,'aim_solver',false); % use AIM algorithm to compute perturbation approximation instead of mjdgges
-options_mom_ = set_default_option(options_mom_,'k_order_solver',false); % use k_order_perturbation instead of mjdgges
-options_mom_ = set_default_option(options_mom_,'dr_cycle_reduction',false); % use cycle reduction algorithm to solve the polynomial equation for retrieving the coefficients associated to the endogenous variables in the decision rule
-options_mom_ = set_default_option(options_mom_,'dr_cycle_reduction_tol',1e-7); % convergence criterion used in the cycle reduction algorithm
-options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction',false); % use logarithmic reduction algorithm to solve the polynomial equation for retrieving the coefficients associated to the endogenous variables in the decision rule
-options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction_maxiter',100); % maximum number of iterations used in the logarithmic reduction algorithm
-options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction_tol',1e-12); % convergence criterion used in the cycle reduction algorithm
-options_mom_ = set_default_option(options_mom_,'lyapunov_db',false); % doubling algorithm (disclyap_fast) to solve Lyapunov equation to compute variance-covariance matrix of state variables
-options_mom_ = set_default_option(options_mom_,'lyapunov_fp',false); % fixed-point algorithm to solve Lyapunov equation to compute variance-covariance matrix of state variables
-options_mom_ = set_default_option(options_mom_,'lyapunov_srs',false); % square-root-solver (dlyapchol) algorithm to solve Lyapunov equation to compute variance-covariance matrix of state variables
-options_mom_ = set_default_option(options_mom_,'lyapunov_complex_threshold',1e-15); % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
-options_mom_ = set_default_option(options_mom_,'lyapunov_fixed_point_tol',1e-10); % convergence criterion used in the fixed point Lyapunov solver
-options_mom_ = set_default_option(options_mom_,'lyapunov_doubling_tol',1e-16); % convergence criterion used in the doubling algorithm
-options_mom_ = set_default_option(options_mom_,'sylvester_fp',false); % determines whether to use fixed point algorihtm to solve Sylvester equation (gensylv_fp), faster for large scale models
-options_mom_ = set_default_option(options_mom_,'sylvester_fixed_point_tol',1e-12); % convergence criterion used in the fixed point Sylvester solver
-options_mom_ = set_default_option(options_mom_,'qz_criterium',1-1e-6); % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
- % if there are no unit roots one can use 1.0 (or slightly below) which we set as default; if they are possible, you may have have multiple unit roots and the accuracy decreases when computing the eigenvalues in lyapunov_symm
- % Note that unit roots are only possible at first-order, at higher order we set it to 1 in pruned_state_space_system and focus only on stationary observables.
-options_mom_ = set_default_option(options_mom_,'qz_zero_threshold',1e-6); % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
-options_mom_ = set_default_option(options_mom_,'schur_vec_tol',1e-11); % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix.
-options_mom_ = set_default_option(options_mom_,'trust_region_initial_step_bound_factor',1); % used in dynare_solve for trust_region
-if options_mom_.order > 2
- fprintf('Dynare will use ''k_order_solver'' as the order>2\n');
- options_mom_.k_order_solver = true;
-end
% -------------------------------------------------------------------------
-% Step 1b: Options that are set by the preprocessor and need to be carried over
+% initializations
% -------------------------------------------------------------------------
-
-% options related to VAROBS
-if ~isfield(options_,'varobs')
- error('method_of_moments: VAROBS statement is missing!')
-else
- options_mom_.varobs = options_.varobs; % observable variables in declaration order
- options_mom_.obs_nbr = length(options_mom_.varobs); % number of observed variables
- % Check that each declared observed variable is also an endogenous variable
- for i = 1:options_mom_.obs_nbr
- if ~any(strcmp(options_mom_.varobs{i},M_.endo_names))
- error(['method_of_moments: Unknown variable (' options_mom_.varobs{i} ')!'])
- end
- end
-
- % Check that a variable is not declared as observed more than once
- if length(unique(options_mom_.varobs))<length(options_mom_.varobs)
- for i = 1:options_mom_.obs_nbr
- if sum(strcmp(options_mom_.varobs{i},options_mom_.varobs))>1
- error(['method_of_moments: A variable cannot be declared as observed more than once (' options_mom_.varobs{i} ')!'])
- end
- end
- end
+% create output directories to store results
+CheckPath('method_of_moments',M_.dname);
+CheckPath('graphs',options_mom_.dirname);
+% initialize options that might change
+options_mom_.mom.compute_derivs = false; % flag to compute derivs in objective function (might change for GMM with either analytic_standard_errors or analytic_jacobian (dependent on optimizer))
+options_mom_.mom.vector_output = false; % specifies whether the objective function returns a vector
+% decision rule
+oo_.dr = set_state_space(oo_.dr,M_,options_mom_); % get state-space representation
+oo_.mom.obs_var = []; % create index of observed variables in DR order
+for i = 1:options_mom_.obs_nbr
+ oo_.mom.obs_var = [oo_.mom.obs_var; find(strcmp(options_mom_.varobs{i}, M_.endo_names(oo_.dr.order_var)))];
end
-% options related to variable declarations
-if isfield(options_,'trend_coeffs')
- error('method_of_moments: %s does not allow for trend in data',options_mom_.mom.mom_method)
-end
-
-% options related to estimated_params and estimated_params_init
-options_mom_.use_calibration_initialization = options_.use_calibration_initialization;
-
-% options related to model block
-options_mom_.linear = options_.linear;
-options_mom_.use_dll = options_.use_dll;
-options_mom_.block = options_.block;
-options_mom_.bytecode = options_.bytecode;
-
-% options related to steady command
-options_mom_.homotopy_force_continue = options_.homotopy_force_continue;
-options_mom_.homotopy_mode = options_.homotopy_mode;
-options_mom_.homotopy_steps = options_.homotopy_steps;
-options_mom_.markowitz = options_.markowitz;
-options_mom_.solve_algo = options_.solve_algo;
-options_mom_.solve_tolf = options_.solve_tolf;
-options_mom_.solve_tolx = options_.solve_tolx;
-options_mom_.steady = options_.steady;
-options_mom_.steadystate = options_.steadystate;
-options_mom_.steadystate_flag = options_.steadystate_flag;
-
-% options related to dataset
-options_mom_.dataset = options_.dataset;
-options_mom_.initial_period = options_.initial_period;
-
-% options related to endogenous prior restrictions are not supported
-options_mom_.endogenous_prior_restrictions.irf = {};
-options_mom_.endogenous_prior_restrictions.moment = {};
-if ~isempty(options_.endogenous_prior_restrictions.irf) && ~isempty(options_.endogenous_prior_restrictions.moment)
- fprintf('Endogenous prior restrictions are not supported yet and will be skipped.\n')
-end
% -------------------------------------------------------------------------
-% Step 1c: Options related to optimizers
+% matched_moments: checks and transformations
% -------------------------------------------------------------------------
-% mode_compute = 1, 3, 7, 11, 102, 11, 13
-% nothing to be done
-% mode_compute = 2
-options_mom_.saopt = options_.saopt;
-% mode_compute = 4
-options_mom_.csminwel = options_.csminwel;
-% mode_compute = 5
-options_mom_.newrat = options_.newrat;
-options_mom_.gstep = options_.gstep;
-% mode_compute = 6
-options_mom_.gmhmaxlik = options_.gmhmaxlik;
-options_mom_.mh_jscale = options_.mh_jscale;
-% mode_compute = 8
-options_mom_.simplex = options_.simplex;
-% mode_compute = 9
-options_mom_.cmaes = options_.cmaes;
-% mode_compute = 10
-options_mom_.simpsa = options_.simpsa;
-% mode_compute = 12
-options_mom_.particleswarm = options_.particleswarm;
-% mode_compute = 101
-options_mom_.solveopt = options_.solveopt;
-
-options_mom_.gradient_method = options_.gradient_method;
-options_mom_.gradient_epsilon = options_.gradient_epsilon;
-options_mom_.analytic_derivation = 0;
-options_mom_.analytic_derivation_mode = 0; % needed by get_perturbation_params_derivs.m, ie use efficient sylvester equation method to compute analytical derivatives as in Ratto & Iskrev (2012)
-
-options_mom_.vector_output= false; % specifies whether the objective function returns a vector
-
-optimizer_vec=[options_mom_.mode_compute;num2cell(options_mom_.additional_optimizer_steps)]; % at each stage one can possibly use different optimizers sequentially
-
-analytic_jacobian_optimizers = [1, 3, 4, 13, 101]; %these are currently supported, see to-do list
-
-% -------------------------------------------------------------------------
-% Step 1d: Other options that need to be initialized
-% -------------------------------------------------------------------------
-options_mom_.initialize_estimated_parameters_with_the_prior_mode = 0; % needed by set_prior.m
-options_mom_.figures.textwidth = 0.8; %needed by plot_priors.m
-options_mom_.ramsey_policy = 0; % needed by evaluate_steady_state
-options_mom_ = set_default_option(options_mom_,'debug',false); %neeeded by e.g. check_plot
-options_mom_.risky_steadystate = false; %needed by resol
-options_mom_.threads = options_.threads; %needed by resol
-options_mom_.jacobian_flag = true;
-options_mom_.gstep = options_.gstep;
-
-% options_mom.dsge_var = false; %needed by check_list_of_variables
-% options_mom.bayesian_irf = false; %needed by check_list_of_variables
-% options_mom.moments_varendo = false; %needed by check_list_of_variables
-% options_mom.smoother = false; %needed by check_list_of_variables
-% options_mom.filter_step_ahead = []; %needed by check_list_of_variables
-% options_mom.forecast = 0;
-%options_mom_ = set_default_option(options_mom_,'endo_vars_for_moment_computations_in_estimation',[]);
-
-% -------------------------------------------------------------------------
-% Step 1e: Get variable orderings and state space representation
-% -------------------------------------------------------------------------
-oo_.dr = set_state_space(oo_.dr,M_,options_mom_);
-% Get index of observed variables in DR order
-oo_.dr.obs_var = [];
-for i=1:options_mom_.obs_nbr
- oo_.dr.obs_var = [oo_.dr.obs_var; find(strcmp(options_mom_.varobs{i}, M_.endo_names(oo_.dr.order_var)))];
-end
-
-% -------------------------------------------------------------------------
-% Step 2: Checks and transformations for matched_moments structure
-% -------------------------------------------------------------------------
-M_.matched_moments_orig = M_.matched_moments; %save original structure
-
-% higher-order product moments not supported yet for GMM
-if strcmp(options_mom_.mom.mom_method, 'GMM') && any(cellfun(@sum,M_.matched_moments(:,3))> 2)
- error('method_of_moments: GMM does not yet support product moments higher than 2. Change row %d in ''matched_moments'' block.',jm);
-end
-
-% check for duplicate moment conditions
-for jm=1:size(M_.matched_moments,1)
- % expand powers to vector of ones
- if any(M_.matched_moments{jm,3}>1)
- tmp1=[]; tmp2=[]; tmp3=[];
- for jjm=1:length(M_.matched_moments{jm,3})
- tmp1 = [tmp1 repmat(M_.matched_moments{jm,1}(jjm),[1 M_.matched_moments{jm,3}(jjm)]) ];
- tmp2 = [tmp2 repmat(M_.matched_moments{jm,2}(jjm),[1 M_.matched_moments{jm,3}(jjm)]) ];
- tmp3 = [tmp3 repmat(1,[1 M_.matched_moments{jm,3}(jjm)]) ];
- end
- M_.matched_moments{jm,1} = tmp1;
- M_.matched_moments{jm,2} = tmp2;
- M_.matched_moments{jm,3} = tmp3;
+if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
+ M_.matched_moments = mom.matched_moments_block(M_.matched_moments, options_mom_.mom.mom_method);
+ % Check if both prefilter and first moments were specified
+ first_moment_indicator = find(cellfun(@(x) sum(abs(x))==1,M_.matched_moments(:,3)))';
+ if options_mom_.prefilter && ~isempty(first_moment_indicator)
+ fprintf('Centered moments requested (prefilter option is set); therefore, ignore declared first moments in ''matched_moments'' block.\n');
+ M_.matched_moments(first_moment_indicator,:)=[]; %remove first moments entries
+ end
+ options_mom_.mom.mom_nbr = size(M_.matched_moments,1);
+ % Get maximum lag number for autocovariances/autocorrelations
+ options_mom_.ar = max(cellfun(@max,M_.matched_moments(:,2))) - min(cellfun(@min,M_.matched_moments(:,2)));
+ % Check that only observed variables are involved in moments
+ not_observed_variables=setdiff(oo_.dr.inv_order_var([M_.matched_moments{:,1}]),oo_.mom.obs_var);
+ if ~isempty(not_observed_variables)
+ skipline;
+ error('method_of_moments: You specified moments involving %s, but it is not a varobs!',M_.endo_names{oo_.dr.order_var(not_observed_variables)})
end
- % shift time structure to focus only on lags
- M_.matched_moments{jm,2} = M_.matched_moments{jm,2} - max(M_.matched_moments{jm,2});
- % sort such that t=0 variable comes first
- [M_.matched_moments{jm,2},idx_sort] = sort(M_.matched_moments{jm,2},'descend');
- M_.matched_moments{jm,1} = M_.matched_moments{jm,1}(idx_sort);
- M_.matched_moments{jm,3} = M_.matched_moments{jm,3}(idx_sort);
-end
-
-% find duplicate rows in cell array by making groups according to powers as we can then use cell2mat for the unique function
-powers = cellfun(@sum,M_.matched_moments(:,3))';
-UniqueMomIdx = [];
-for jpow = unique(powers)
- idx1 = find(powers==jpow);
- [~,idx2] = unique(cell2mat(M_.matched_moments(idx1,:)),'rows');
- UniqueMomIdx = [UniqueMomIdx idx1(idx2)];
-end
-
-% remove duplicate elements
-DuplicateMoms = setdiff(1:size(M_.matched_moments_orig,1),UniqueMomIdx);
-if ~isempty(DuplicateMoms)
- fprintf('Found and removed duplicate declared moments in ''matched_moments'' block in rows:\n %s.\n',num2str(DuplicateMoms))
- fprintf('Dynare will continue with remaining moment conditions\n');
-end
-
-if strcmp(options_mom_.mom.mom_method, 'SMM')
- % for SMM we can keep the original structure but get rid of duplicate moments
- M_.matched_moments = M_.matched_moments_orig(sort(UniqueMomIdx),:);
-elseif strcmp(options_mom_.mom.mom_method, 'GMM')
- % for GMM we use the transformed matched_moments structure
- M_.matched_moments = M_.matched_moments(sort(UniqueMomIdx),:);
-end
-
-% Check if both prefilter and first moments were specified
-first_moment_indicator = find(cellfun(@(x) sum(abs(x))==1,M_.matched_moments(:,3)))';
-if options_mom_.prefilter && ~isempty(first_moment_indicator)
- fprintf('Centered moments requested (prefilter option is set); therefore, ignore declared first moments in ''matched_moments'' block.\n');
- M_.matched_moments(first_moment_indicator,:)=[]; %remove first moments entries
end
-options_mom_.mom.mom_nbr = size(M_.matched_moments,1);
-% Get maximum lag number for autocovariances/autocorrelations
-options_mom_.ar = max(cellfun(@max,M_.matched_moments(:,2))) - min(cellfun(@min,M_.matched_moments(:,2)));
-
-%check that only observed variables are involved in moments
-not_observed_variables=setdiff(oo_.dr.inv_order_var([M_.matched_moments{:,1}]),oo_.dr.obs_var);
-if ~isempty(not_observed_variables)
- error('\nmethod_of_moments: You specified moments involving %s, but it is not a varobs.',M_.endo_names{oo_.dr.order_var(not_observed_variables)})
-end
% -------------------------------------------------------------------------
-% Step 3: Checks and transformations for estimated parameters, priors, and bounds
+% estimated parameters: checks and transformations on values, priors, bounds
% -------------------------------------------------------------------------
-
-% Set priors and bounds over the estimated parameters
+% set priors and bounds over the estimated parameters
[xparam0, estim_params_, bayestopt_, lb, ub, M_] = set_prior(estim_params_, M_, options_mom_);
+number_of_estimated_parameters = length(xparam0);
+hessian_xparam0 = []; % initialize hessian
-% Check measurement errors
-if (estim_params_.nvn || estim_params_.ncn) && strcmp(options_mom_.mom.mom_method, 'GMM')
- error('method_of_moments: GMM estimation does not support measurement error(s) yet. Please specifiy them as a structural shock.')
-end
-
-% Check if enough moments for estimation
-if options_mom_.mom.mom_nbr < length(xparam0)
- fprintf('\n');
- error('method_of_moments: We must have at least as many moments as parameters for a method of moments estimation.')
+% check if enough moments for estimation
+if strcmp(options_mom_.mom.mom_method, 'GMM') || strcmp(options_mom_.mom.mom_method, 'SMM')
+ if options_mom_.mom.mom_nbr < length(xparam0)
+ skipline;
+ error('method_of_moments: There must be at least as many moments as parameters for a %s estimation!',options_mom_.mom.mom_method);
+ end
+ skipline(2);
end
-fprintf('\n\n')
-% Check if a _prior_restrictions.m file exists
+% check if a _prior_restrictions.m file exists
if exist([M_.fname '_prior_restrictions.m'],'file')
options_mom_.prior_restrictions.status = 1;
options_mom_.prior_restrictions.routine = str2func([M_.fname '_prior_restrictions']);
end
-bayestopt_laplace=bayestopt_;
-
-% Check on specified priors and penalized estimation
-if any(bayestopt_laplace.pshape > 0) % prior specified
- if ~options_mom_.mom.penalized_estimator
- fprintf('\nPriors were specified, but the penalized_estimator-option was not set.\n')
- fprintf('Dynare sets penalized_estimator to 1. Conducting %s with penalty.\n',options_mom_.mom.mom_method)
- options_mom_.mom.penalized_estimator=1;
- end
- if any(setdiff([0;bayestopt_laplace.pshape],[0,3]))
- fprintf('\nNon-normal priors specified. %s with penalty uses a Laplace type of approximation.\n',options_mom_.mom.mom_method)
- fprintf('Only the prior mean and standard deviation are relevant, all other shape information, except for the parameter bounds, is ignored.\n\n')
- non_normal_priors=bayestopt_laplace.pshape~=3;
- bayestopt_laplace.pshape(non_normal_priors) = 3;
- bayestopt_laplace.p3(non_normal_priors) = -Inf*ones(sum(non_normal_priors),1);
- bayestopt_laplace.p4(non_normal_priors) = Inf*ones(sum(non_normal_priors),1);
- bayestopt_laplace.p6(non_normal_priors) = bayestopt_laplace.p1(non_normal_priors);
- bayestopt_laplace.p7(non_normal_priors) = bayestopt_laplace.p2(non_normal_priors);
- bayestopt_laplace.p5(non_normal_priors) = bayestopt_laplace.p1(non_normal_priors);
- end
- if any(isinf(bayestopt_laplace.p2)) %find infinite variance priors
- inf_var_pars=bayestopt_laplace.name(isinf(bayestopt_laplace.p2));
- disp_string=[inf_var_pars{1,:}];
- for ii=2:size(inf_var_pars,1)
- disp_string=[disp_string,', ',inf_var_pars{ii,:}];
- end
- fprintf('The parameter(s) %s have infinite prior variance. This implies a flat prior\n',disp_string)
- fprintf('Dynare disables the matrix singularity warning\n')
- if isoctave
- warning('off','Octave:singular-matrix');
- else
- warning('off','MATLAB:singularMatrix');
+% check on specified priors and penalized estimation (which uses Laplace approximated priors)
+if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
+ bayestopt_orig = bayestopt_;
+ if any(bayestopt_.pshape > 0) % prior specified
+ if ~options_mom_.mom.penalized_estimator
+ fprintf('\nPriors were specified, but the penalized_estimator-option was not set.');
+ fprintf('\nDynare sets penalized_estimator to 1. Conducting %s with penalty.\n',options_mom_.mom.mom_method);
+ options_mom_.mom.penalized_estimator = 1;
end
+ bayestopt_ = mom.transform_prior_to_laplace_prior(bayestopt_);
end
end
-% Check for calibrated covariances before updating parameters
+% check for calibrated covariances before updating parameters
estim_params_ = check_for_calibrated_covariances(xparam0,estim_params_,M_);
-% Checks on parameter calibration and initialization
-xparam1_calib = get_all_parameters(estim_params_,M_); %get calibrated parameters
-if ~any(isnan(xparam1_calib)) %all estimated parameters are calibrated
- estim_params_.full_calibration_detected=1;
+% checks on parameter calibration and initialization
+xparam_calib = get_all_parameters(estim_params_,M_); % get calibrated parameters
+if ~any(isnan(xparam_calib)) % all estimated parameters are calibrated
+ estim_params_.full_calibration_detected = true;
else
- estim_params_.full_calibration_detected=0;
+ estim_params_.full_calibration_detected = false;
end
-if options_mom_.use_calibration_initialization %set calibration as starting values
- if ~isempty(bayestopt_laplace) && all(bayestopt_laplace.pshape==0) && any(all(isnan([xparam1_calib xparam0]),2))
- error('method_of_moments: When using the use_calibration option with %s without prior, the parameters must be explicitly initialized.',options_mom_.mom.mom_method)
+if options_mom_.use_calibration_initialization % set calibration as starting values
+ if ~isempty(bayestopt_) && ~doBayesianEstimation && any(all(isnan([xparam_calib xparam0]),2))
+ error('method_of_moments: When using the use_calibration option with %s without prior, the parameters must be explicitly initialized!',options_mom_.mom.mom_method);
else
- [xparam0,estim_params_]=do_parameter_initialization(estim_params_,xparam1_calib,xparam0); %get explicitly initialized parameters that have precedence over calibrated values
+ [xparam0,estim_params_] = do_parameter_initialization(estim_params_,xparam_calib,xparam0); % get explicitly initialized parameters that have precedence over calibrated values
end
end
-% Check initialization
-if ~isempty(bayestopt_laplace) && all(bayestopt_laplace.pshape==0) && any(isnan(xparam0))
- error('method_of_moments: %s without penalty requires all estimated parameters to be initialized, either in an estimated_params or estimated_params_init-block ',options_mom_.mom.mom_method)
+% check initialization
+if ~isempty(bayestopt_) && ~doBayesianEstimation && any(isnan(xparam0))
+ error('method_of_moments: Frequentist %s requires all estimated parameters to be initialized, either in an estimated_params or estimated_params_init-block!',options_mom_.mom.mom_method);
end
-% Set and check parameter bounds
-if ~isempty(bayestopt_laplace) && any(bayestopt_laplace.pshape > 0)
- % Plot prior densities
+% set and check parameter bounds
+if ~isempty(bayestopt_) && doBayesianEstimation
+ % plot prior densities
if ~options_mom_.nograph && options_mom_.plot_priors
- plot_priors(bayestopt_,M_,estim_params_,options_mom_)
- plot_priors(bayestopt_laplace,M_,estim_params_,options_mom_,'Laplace approximated priors')
+ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
+ plot_priors(bayestopt_orig,M_,estim_params_,options_mom_,'Original priors'); % only for visual inspection (not saved to disk, because overwritten in next call to plot_priors)
+ plot_priors(bayestopt_,M_,estim_params_,options_mom_,'Laplace approximated priors');
+ clear('bayestopt_orig'); % make sure stale structure cannot be used
+ end
end
- % Set prior bounds
- Bounds = prior_bounds(bayestopt_laplace, options_mom_.prior_trunc);
+ % set prior bounds
+ Bounds = prior_bounds(bayestopt_, options_mom_.prior_trunc);
Bounds.lb = max(Bounds.lb,lb);
Bounds.ub = min(Bounds.ub,ub);
-else % estimated parameters but no declared priors
- % No priors are declared so Dynare will estimate the parameters
- % with inequality constraints for the parameters.
+else
+ % no priors are declared so Dynare will estimate the parameters with
+ % Frequentist methods using inequality constraints for the parameters
Bounds.lb = lb;
Bounds.ub = ub;
if options_mom_.mom.penalized_estimator
fprintf('Penalized estimation turned off as you did not declare priors\n')
options_mom_.mom.penalized_estimator = 0;
- end
+ end
end
-% Set correct bounds for standard deviations and corrrelations
-param_of_interest=(1:length(xparam0))'<=estim_params_.nvx+estim_params_.nvn;
-LB_below_0=(Bounds.lb<0 & param_of_interest);
-Bounds.lb(LB_below_0)=0;
-param_of_interest=(1:length(xparam0))'> estim_params_.nvx+estim_params_.nvn & (1:length(xparam0))'<estim_params_.nvx+estim_params_.nvn +estim_params_.ncx + estim_params_.ncn;
-LB_below_minus_1=(Bounds.lb<-1 & param_of_interest);
-UB_above_1=(Bounds.ub>1 & param_of_interest);
-Bounds.lb(LB_below_minus_1)=-1;
-Bounds.ub(UB_above_1)=1;
-clear('bayestopt_','LB_below_0','LB_below_minus_1','UB_above_1','param_of_interest');%make sure stale structure cannot be used
+% set correct bounds for standard deviations and correlations
+Bounds = mom.set_correct_bounds_for_stderr_corr(estim_params_,Bounds);
-% Test if initial values of the estimated parameters are all between the prior lower and upper bounds
+% test if initial values of the estimated parameters are all between the prior lower and upper bounds
if options_mom_.use_calibration_initialization
try
- check_prior_bounds(xparam0,Bounds,M_,estim_params_,options_mom_,bayestopt_laplace)
+ check_prior_bounds(xparam0,Bounds,M_,estim_params_,options_mom_,bayestopt_);
catch last_error
fprintf('Cannot use parameter values from calibration as they violate the prior bounds.')
rethrow(last_error);
end
else
- check_prior_bounds(xparam0,Bounds,M_,estim_params_,options_mom_,bayestopt_laplace)
+ check_prior_bounds(xparam0,Bounds,M_,estim_params_,options_mom_,bayestopt_);
end
-estim_params_= get_matrix_entries_for_psd_check(M_,estim_params_);
+% check for positive definiteness
+estim_params_ = get_matrix_entries_for_psd_check(M_,estim_params_);
-% Set sigma_e_is_diagonal flag (needed if the shocks block is not declared in the mod file).
+% set sigma_e_is_diagonal flag (needed if the shocks block is not declared in the mod file)
M_.sigma_e_is_diagonal = true;
if estim_params_.ncx || any(nnz(tril(M_.Correlation_matrix,-1))) || isfield(estim_params_,'calibrated_covariances')
M_.sigma_e_is_diagonal = false;
end
-% storing prior parameters in MoM info structure for penalized minimization
-oo_.prior.mean = bayestopt_laplace.p1;
-oo_.prior.variance = diag(bayestopt_laplace.p2.^2);
+% storing prior parameters in results
+oo_.mom.prior.mean = bayestopt_.p1;
+oo_.mom.prior.mode = bayestopt_.p5;
+oo_.mom.prior.variance = diag(bayestopt_.p2.^2);
+oo_.mom.prior.hyperparameters.first = bayestopt_.p6;
+oo_.mom.prior.hyperparameters.second = bayestopt_.p7;
-% Set all parameters
+% set all parameters
M_ = set_all_parameters(xparam0,estim_params_,M_);
-%provide warning if there is NaN in parameters
+% provide warning if there is NaN in parameters
test_for_deep_parameters_calibration(M_);
-% -------------------------------------------------------------------------
-% Step 4: Checks and transformations for data
-% -------------------------------------------------------------------------
+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 if datafile has same name as mod file
-[~,name,~] = fileparts(options_mom_.datafile);
-if strcmp(name,M_.fname)
- error('method_of_moments: Data-file and mod-file are not allowed to have the same name. Please change the name of the data file.')
+ % 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
-% Build dataset
-dataset_ = makedataset(options_mom_);
-
-% set options for old interface from the ones for new interface
-if ~isempty(dataset_)
- options_mom_.nobs = dataset_.nobs;
-end
-% Check length of data for estimation of second moments
-if options_mom_.ar > options_mom_.nobs+1
- error('method_of_moments: Data set is too short to compute second moments');
+% -------------------------------------------------------------------------
+% datafile: checks and transformations
+% -------------------------------------------------------------------------
+% Build dataset
+if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
+ % Check if datafile has same name as mod file
+ [~,name,~] = fileparts(options_mom_.datafile);
+ if strcmp(name,M_.fname)
+ error('method_of_moments: ''datafile'' and mod file are not allowed to have the same name; change the name of the ''datafile''!')
+ end
+ dataset_ = makedataset(options_mom_);
+ % set options for old interface from the ones for new interface
+ if ~isempty(dataset_)
+ options_mom_.nobs = dataset_.nobs;
+ end
+ % Check length of data for estimation of second moments
+ if options_mom_.ar > options_mom_.nobs+1
+ error('method_of_moments: Dataset is too short to compute higher than first moments!');
+ end
+ % Provide info on data moments handling
+ fprintf('Computing data moments. Note that NaN values in the moments (due to leads and lags or missing data) are replaced by the mean of the corresponding moment.\n');
+ % Get data moments for the method of moments
+ [oo_.mom.data_moments, oo_.mom.m_data] = mom.get_data_moments(dataset_.data, oo_, M_.matched_moments, options_mom_);
+ if ~isreal(dataset_.data)
+ error('method_of_moments: The data moments contain complex values!')
+ end
end
-% Provide info on data moments handling
-fprintf('Computing data moments. Note that NaN values in the moments (due to leads and lags or missing data) are replaced by the mean of the corresponding moment\n');
-% Get data moments for the method of moments
-[oo_.mom.data_moments, oo_.mom.m_data] = mom.data_moments(dataset_.data, oo_, M_.matched_moments, options_mom_);
-
-% Get shock series for SMM and set variance correction factor
+% -------------------------------------------------------------------------
+% SMM: Get shock series fand set variance correction factor
+% -------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'SMM')
options_mom_.mom.long = round(options_mom_.mom.simulation_multiple*options_mom_.nobs);
options_mom_.mom.variance_correction_factor = (1+1/options_mom_.mom.simulation_multiple);
@@ -659,7 +457,7 @@ if strcmp(options_mom_.mom.mom_method,'SMM')
[state_u,state_n] = get_dynare_random_generator_state; %get state for later resetting
set_dynare_random_generator_state(options_mom_.mom.seed,options_mom_.mom.seed);
temp_shocks = randn(options_mom_.mom.long+options_mom_.mom.burnin,M_.exo_nbr);
- temp_shocks_ME = randn(options_mom_.mom.long,length(M_.H));
+ temp_shocks_ME = randn(options_mom_.mom.long,length(M_.H));
set_dynare_random_generator_state(state_u,state_n); %reset state for later resetting
end
if options_mom_.mom.bounded_shock_support == 1
@@ -675,84 +473,60 @@ if strcmp(options_mom_.mom.mom_method,'SMM')
end
end
+
% -------------------------------------------------------------------------
-% Step 5: checks for steady state at initial parameters
+% checks for steady state at initial parameters
% -------------------------------------------------------------------------
-% setting steadystate_check_flag option
+% check if steady state solves static model and if steady-state changes estimated parameters
if options_mom_.steadystate.nocheck
- steadystate_check_flag = 0;
+ steadystate_check_flag_vec = [0 1];
else
- steadystate_check_flag = 1;
+ steadystate_check_flag_vec = [1 1];
end
-
-old_steady_params=M_.params; %save initial parameters for check if steady state changes param values
-% Check steady state at initial model parameter values
-[oo_.steady_state, new_steady_params, info] = evaluate_steady_state(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],M_,options_mom_,steadystate_check_flag);
+[oo_.steady_state, info, steady_state_changes_parameters] = check_steady_state_changes_parameters(M_, estim_params_, oo_, options_mom_, steadystate_check_flag_vec);
if info(1)
- fprintf('\nmethod_of_moments: The steady state at the initial parameters cannot be computed.\n')
+ fprintf('\nThe steady state at the initial parameters cannot be computed.\n')
print_info(info, 0, options_mom_);
end
-
-% check whether steady state file changes estimated parameters
-if isfield(estim_params_,'param_vals') && ~isempty(estim_params_.param_vals)
- Model_par_varied=M_; %store M_ structure
-
- Model_par_varied.params(estim_params_.param_vals(:,1))=Model_par_varied.params(estim_params_.param_vals(:,1))*1.01; %vary parameters
- [~, new_steady_params_2] = evaluate_steady_state(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],Model_par_varied,options_mom_,true);
-
- changed_par_indices=find((old_steady_params(estim_params_.param_vals(:,1))-new_steady_params(estim_params_.param_vals(:,1))) ...
- | (Model_par_varied.params(estim_params_.param_vals(:,1))-new_steady_params_2(estim_params_.param_vals(:,1))));
-
- if ~isempty(changed_par_indices)
- fprintf('\nThe steady state file internally changed the values of the following estimated parameters:\n')
- disp(char(M_.param_names(estim_params_.param_vals(changed_par_indices,1))))
- fprintf('This will override parameter values and may lead to wrong results.\n')
- fprintf('Check whether this is really intended.\n')
- warning('The steady state file internally changes the values of the estimated parameters.')
- if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors
- fprintf('For analytical standard errors, the parameter-Jacobians of the dynamic model and of the steady-state will be computed numerically\n'),
- fprintf('(re-set options_mom_.analytic_derivation_mode= -2)'),
- options_mom_.analytic_derivation_mode= -2;
- end
- end
+if steady_state_changes_parameters && strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors
+ fprintf('For analytical standard errors, the parameter-Jacobians of the dynamic model and of the steady-state will be computed numerically,\n');
+ fprintf('because the steady-state changes estimated parameters. Option ''analytic_derivation_mode'' reset to -2.');
+ options_mom_.analytic_derivation_mode = -2;
end
% display warning if some parameters are still NaN
test_for_deep_parameters_calibration(M_);
-% If steady state of observed variables is non zero, set noconstant equal 0
-if all(abs(oo_.steady_state(oo_.dr.order_var(oo_.dr.obs_var)))<1e-9)
- options_mom_.noconstant = 0; %identifying the constant based on just the initial parameter value is not feasible
-else
- options_mom_.noconstant = 0;
-end
% -------------------------------------------------------------------------
-% Step 6: checks for objective function at initial parameters
+% checks for objective function at initial parameters
% -------------------------------------------------------------------------
objective_function = str2func('mom.objective_function');
-
try
- % Check for NaN or complex values of moment-distance-funtion evaluated
- % at initial parameters and identity weighting matrix
- oo_.mom.Sw = eye(options_mom_.mom.mom_nbr);
- tic_id = tic;
- [fval, info, ~, ~, ~, oo_, M_] = feval(objective_function, xparam0, Bounds, oo_, estim_params_, M_, options_mom_);
- elapsed_time = toc(tic_id);
+ % Check for NaN or complex values of moment-distance-funtion evaluated at initial parameters
+ if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_method,'GMM')
+ oo_.mom.Sw = eye(options_mom_.mom.mom_nbr); % initialize with identity weighting matrix
+ end
+ tic_id = tic;
+ [fval, info, ~, ~, ~, oo_, M_] = feval(objective_function, xparam0, Bounds, oo_, estim_params_, M_, options_mom_, bayestopt_);
+ elapsed_time = toc(tic_id);
if isnan(fval)
- error('method_of_moments: The initial value of the objective function is NaN')
+ error('method_of_moments: The initial value of the objective function with identity weighting matrix is NaN!')
elseif imag(fval)
- error('method_of_moments: The initial value of the objective function is complex')
+ error('method_of_moments: The initial value of the objective function with identity weighting matrix is complex!')
end
if info(1) > 0
disp('method_of_moments: Error in computing the objective function for initial parameter values')
print_info(info, options_mom_.noprint, options_mom_)
end
- fprintf('Initial value of the moment objective function with %4.1f times identity weighting matrix: %6.4f \n\n', options_mom_.mom.weighting_matrix_scaling_factor, fval);
+ fprintf('Initial value of the moment objective function');
+ if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_method,'GMM')
+ fprintf(' with %4.1f times identity weighting matrix', options_mom_.mom.weighting_matrix_scaling_factor);
+ end
+ fprintf(': %6.4f \n\n', fval);
fprintf('Time required to compute objective function once: %5.4f seconds \n', elapsed_time);
-
-catch last_error% if check fails, provide info on using calibration if present
+catch last_error % if check fails, provide info on using calibration if present
if estim_params_.full_calibration_detected %calibrated model present and no explicit starting values
skipline(1);
fprintf('There was an error in computing the moments for initial parameter values.\n')
@@ -765,264 +539,60 @@ catch last_error% if check fails, provide info on using calibration if present
rethrow(last_error);
end
+
% -------------------------------------------------------------------------
-% Step 7a: Method of moments estimation: print some info
+% print some info to console
% -------------------------------------------------------------------------
-fprintf('\n---------------------------------------------------\n')
-if strcmp(options_mom_.mom.mom_method,'SMM')
- fprintf('Simulated method of moments with');
-elseif strcmp(options_mom_.mom.mom_method,'GMM')
- fprintf('General method of moments with');
-end
-if options_mom_.prefilter
- fprintf('\n - centered moments (prefilter=1)');
-else
- fprintf('\n - uncentered moments (prefilter=0)');
-end
-if options_mom_.mom.penalized_estimator
- fprintf('\n - penalized estimation using deviation from prior mean and weighted with prior precision');
-end
+mom.print_info_on_estimation_settings(options_mom_, number_of_estimated_parameters);
-for i = 1:length(optimizer_vec)
- if i == 1
- str = '- optimizer (mode_compute';
- else
- str = ' (additional_optimizer_steps';
- end
- switch optimizer_vec{i}
- case 0
- fprintf('\n %s=0): no minimization',str);
- case 1
- fprintf('\n %s=1): fmincon',str);
- case 2
- fprintf('\n %s=2): continuous simulated annealing',str);
- case 3
- fprintf('\n %s=3): fminunc',str);
- case 4
- fprintf('\n %s=4): csminwel',str);
- case 5
- fprintf('\n %s=5): newrat',str);
- case 6
- fprintf('\n %s=6): gmhmaxlik',str);
- case 7
- fprintf('\n %s=7): fminsearch',str);
- case 8
- fprintf('\n %s=8): Dynare Nelder-Mead simplex',str);
- case 9
- fprintf('\n %s=9): CMA-ES',str);
- case 10
- fprintf('\n %s=10): simpsa',str);
- case 11
- error('\nmethod_of_moments: online_auxiliary_filter (mode_compute=11) is only supported with likelihood-based estimation techniques');
- case 12
- fprintf('\n %s=12): particleswarm',str);
- case 101
- fprintf('\n %s=101): SolveOpt',str);
- case 102
- fprintf('\n %s=102): simulannealbnd',str);
- case 13
- fprintf('\n %s=13): lsqnonlin',str);
- otherwise
- if ischar(optimizer_vec{i})
- fprintf('\n %s=%s): user-defined',str,optimizer_vec{i});
- else
- error('method_of_moments: Unknown optimizer, please contact the developers ')
- end
- end
- if options_mom_.silent_optimizer
- fprintf(' (silent)');
- end
- if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_jacobian && ismember(optimizer_vec{i},analytic_jacobian_optimizers)
- fprintf(' (using analytical Jacobian)');
- end
-end
-fprintf('\n - perturbation order: %d', options_mom_.order)
-if options_mom_.order > 1 && options_mom_.pruning
- fprintf(' (with pruning)')
-end
-if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors
- fprintf('\n - standard errors: analytic derivatives');
-else
- fprintf('\n - standard errors: numerical derivatives');
-end
-fprintf('\n - number of matched moments: %d', options_mom_.mom.mom_nbr);
-fprintf('\n - number of parameters: %d', length(xparam0));
-fprintf('\n\n');
% -------------------------------------------------------------------------
-% Step 7b: Iterated method of moments estimation
+% GMM/SMM: iterated estimation
% -------------------------------------------------------------------------
-if size(options_mom_.mom.weighting_matrix,1)>1 && ~(any(strcmpi('diagonal',options_mom_.mom.weighting_matrix)) || any(strcmpi('optimal',options_mom_.mom.weighting_matrix)))
- fprintf('\nYou did not specify the use of an optimal or diagonal weighting matrix. There is no point in running an iterated method of moments.\n')
-end
-
-for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
- fprintf('Estimation stage %u\n',stage_iter);
- Woptflag = false;
- switch lower(options_mom_.mom.weighting_matrix{stage_iter})
- case 'identity_matrix'
- fprintf(' - identity weighting matrix\n');
- weighting_matrix = eye(options_mom_.mom.mom_nbr);
- case 'diagonal'
- fprintf(' - diagonal of optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
- if stage_iter == 1
- fprintf(' and using data-moments as initial estimate of model-moments\n');
- weighting_matrix = diag(diag( mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag) ));
- else
- fprintf(' and using previous stage estimate of model-moments\n');
- weighting_matrix = diag(diag( mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag) ));
- end
- case 'optimal'
- fprintf(' - optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
- if stage_iter == 1
- fprintf(' and using data-moments as initial estimate of model-moments\n');
- weighting_matrix = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag);
- else
- fprintf(' and using previous stage estimate of model-moments\n');
- weighting_matrix = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
- Woptflag = true;
- end
- otherwise %user specified matrix in file
- fprintf(' - user-specified weighting matrix\n');
- try
- load(options_mom_.mom.weighting_matrix{stage_iter},'weighting_matrix')
- catch
- error(['method_of_moments: No matrix named ''weighting_matrix'' could be found in ',options_mom_.mom.weighting_matrix{stage_iter},'.mat'])
- end
- [nrow, ncol] = size(weighting_matrix);
- if ~isequal(nrow,ncol) || ~isequal(nrow,length(oo_.mom.data_moments)) %check if square and right size
- error(['method_of_moments: weighting_matrix must be square and have ',num2str(length(oo_.mom.data_moments)),' rows and columns'])
- end
- end
- try %check for positive definiteness of weighting_matrix
- oo_.mom.Sw = chol(weighting_matrix);
- catch
- error('method_of_moments: Specified weighting_matrix is not positive definite. Check whether your model implies stochastic singularity.')
- end
-
- for optim_iter= 1:length(optimizer_vec)
- options_mom_.current_optimizer = optimizer_vec{optim_iter};
- if optimizer_vec{optim_iter}==0
- xparam1=xparam0; %no minimization, evaluate objective at current values
- fval = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_);
- else
- if optimizer_vec{optim_iter}==13
- options_mom_.vector_output = true;
- else
- options_mom_.vector_output = false;
- end
- if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_jacobian && ismember(optimizer_vec{optim_iter},analytic_jacobian_optimizers) %do this only for gradient-based optimizers
- options_mom_.mom.compute_derivs = true;
- else
- options_mom_.mom.compute_derivs = false;
- end
-
- [xparam1, fval, exitflag] = dynare_minimize_objective(objective_function, xparam0, optimizer_vec{optim_iter}, options_mom_, [Bounds.lb Bounds.ub], bayestopt_laplace.name, bayestopt_laplace, [],...
- Bounds, oo_, estim_params_, M_, options_mom_);
- if options_mom_.vector_output
- fval = fval'*fval;
- end
- end
- fprintf('\nStage %d Iteration %d: value of minimized moment distance objective function: %12.10f.\n',stage_iter,optim_iter,fval)
- if options_mom_.mom.verbose
- oo_.mom=display_estimation_results_table(xparam1,NaN(size(xparam1)),M_,options_mom_,estim_params_,bayestopt_laplace,oo_.mom,prior_dist_names,sprintf('%s (STAGE %d ITERATION %d) VERBOSE',options_mom_.mom.mom_method,stage_iter,optim_iter),sprintf('verbose_%s_stage_%d_iter_%d',lower(options_mom_.mom.mom_method),stage_iter,optim_iter));
- end
- xparam0=xparam1;
- end
- options_mom_.vector_output = false;
- % Update M_ and DynareResults (in particular to get oo_.mom.model_moments)
- M_ = set_all_parameters(xparam1,estim_params_,M_);
+if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
+ % compute mode
+ [xparam1, oo_, Woptflag] = mom.mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds);
+ % compute standard errors at mode
+ options_mom_.mom.vector_output = false; % make sure flag is reset
+ M_ = set_all_parameters(xparam1,estim_params_,M_); % update M_ and DynareResults (in particular to get oo_.mom.model_moments)
if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors
- options_mom_.mom.compute_derivs = true; % for GMM we compute derivatives analytically in the objective function with this flag
+ options_mom_.mom.compute_derivs = true; % for GMM we compute derivatives analytically in the objective function with this flag
end
- [fval, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_);
+ [~, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_); % compute model moments and oo_.mom.model_moments_params_derivs
options_mom_.mom.compute_derivs = false; % reset to not compute derivatives in objective function during optimization
-
SE = mom.standard_errors(xparam1, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Woptflag);
-
- % Store results in output structure
- oo_.mom = display_estimation_results_table(xparam1,SE,M_,options_mom_,estim_params_,bayestopt_laplace,oo_.mom,prior_dist_names,sprintf('%s (STAGE %u)',options_mom_.mom.mom_method,stage_iter),sprintf('%s_stage_%u',lower(options_mom_.mom.mom_method),stage_iter));
-end
-
-% -------------------------------------------------------------------------
-% Step 8: J test
-% -------------------------------------------------------------------------
-if options_mom_.mom.mom_nbr > length(xparam1)
- %get optimal weighting matrix for J test, if necessary
- if ~Woptflag
- W_opt = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
- oo_j=oo_;
- oo_j.mom.Sw = chol(W_opt);
- [fval] = feval(objective_function, xparam1, Bounds, oo_j, estim_params_, M_, options_mom_);
+ % mode check plots
+ if options_mom_.mode_check.status
+ mom.check_plot(objective_function,xparam1,SE,options_mom_,M_,estim_params_,Bounds,bayestopt_,...
+ Bounds, oo_, estim_params_, M_, options_mom_);
end
-
- % Compute J statistic
- if strcmp(options_mom_.mom.mom_method,'SMM')
- Variance_correction_factor = options_mom_.mom.variance_correction_factor;
- elseif strcmp(options_mom_.mom.mom_method,'GMM')
- Variance_correction_factor=1;
- end
- oo_.mom.J_test.j_stat = dataset_.nobs*Variance_correction_factor*fval/options_mom_.mom.weighting_matrix_scaling_factor;
- oo_.mom.J_test.degrees_freedom = length(oo_.mom.model_moments)-length(xparam1);
- oo_.mom.J_test.p_val = 1-chi2cdf(oo_.mom.J_test.j_stat, oo_.mom.J_test.degrees_freedom);
- fprintf('\nvalue of J-test statistic: %f\n',oo_.mom.J_test.j_stat)
- fprintf('p-value of J-test statistic: %f\n',oo_.mom.J_test.p_val)
end
+
% -------------------------------------------------------------------------
-% Step 9: Display estimation results
+% display final estimation results
% -------------------------------------------------------------------------
-title = ['Comparison of data moments and model moments (',options_mom_.mom.mom_method,')'];
-headers = {'Moment','Data','Model'};
-for jm = 1:size(M_.matched_moments,1)
- lables_tmp = 'E[';
- lables_tmp_tex = 'E \left[ ';
- for jvar = 1:length(M_.matched_moments{jm,1})
- lables_tmp = [lables_tmp M_.endo_names{M_.matched_moments{jm,1}(jvar)}];
- lables_tmp_tex = [lables_tmp_tex, '{', M_.endo_names_tex{M_.matched_moments{jm,1}(jvar)}, '}'];
- if M_.matched_moments{jm,2}(jvar) ~= 0
- lables_tmp = [lables_tmp, '(', num2str(M_.matched_moments{jm,2}(jvar)), ')'];
- lables_tmp_tex = [lables_tmp_tex, '_{t', num2str(M_.matched_moments{jm,2}(jvar)), '}'];
- else
- lables_tmp_tex = [lables_tmp_tex, '_{t}'];
- end
- if M_.matched_moments{jm,3}(jvar) > 1
- lables_tmp = [lables_tmp, '^', num2str(M_.matched_moments{jm,3}(jvar))];
- lables_tmp_tex = [lables_tmp_tex, '^{', num2str(M_.matched_moments{jm,3}(jvar)) '}'];
- end
- if jvar == length(M_.matched_moments{jm,1})
- lables_tmp = [lables_tmp, ']'];
- lables_tmp_tex = [lables_tmp_tex, ' \right]'];
- else
- lables_tmp = [lables_tmp, '*'];
- lables_tmp_tex = [lables_tmp_tex, ' \times '];
- end
- end
- labels{jm,1} = lables_tmp;
- labels_TeX{jm,1} = lables_tmp_tex;
-end
-data_mat=[oo_.mom.data_moments oo_.mom.model_moments ];
-dyntable(options_mom_, title, headers, labels, data_mat, cellofchararraymaxlength(labels)+2, 10, 7);
-if options_mom_.TeX
- dyn_latex_table(M_, options_mom_, title, ['comparison_moments_', options_mom_.mom.mom_method], headers, labels_TeX, data_mat, cellofchararraymaxlength(labels)+2, 10, 7);
-end
-
-if options_mom_.mode_check.status
- mom.check_plot(objective_function,xparam1,SE,options_mom_,M_,estim_params_,Bounds,bayestopt_laplace,...
- Bounds, oo_, estim_params_, M_, options_mom_)
+if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_method,'GMM')
+ % Store results in output structure
+ oo_.mom = display_estimation_results_table(xparam1,SE,M_,options_mom_,estim_params_,bayestopt_,oo_.mom,prior_dist_names,options_mom_.mom.mom_method,lower(options_mom_.mom.mom_method));
+ % J test
+ oo_ = mom.Jtest(xparam1, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, dataset_.nobs);
+ % display comparison of model moments and data moments
+ mom.display_comparison_moments(M_, options_mom_, oo_.mom.data_moments, oo_.mom.model_moments);
end
-fprintf('\n==== Method of Moments Estimation (%s) Completed ====\n\n',options_mom_.mom.mom_method)
% -------------------------------------------------------------------------
-% Step 9: Clean up
+% clean up
% -------------------------------------------------------------------------
+fprintf('\n==== Method of Moments Estimation (%s) Completed ====\n\n',options_mom_.mom.mom_method)
+
%reset warning state
warning_config;
if isoctave && isfield(options_, 'prior_restrictions') && ...
- isfield(options_.prior_restrictions, 'routine')
+ isfield(options_mom_.prior_restrictions, 'routine')
% Octave crashes if it tries to save function handles (to the _results.mat file)
% See https://savannah.gnu.org/bugs/?43215
- options_.prior_restrictions.routine = [];
-end
+ options_mom_.prior_restrictions.routine = [];
+end
\ No newline at end of file
diff --git a/matlab/+mom/set_correct_bounds_for_stderr_corr.m b/matlab/+mom/set_correct_bounds_for_stderr_corr.m
new file mode 100644
index 0000000000000000000000000000000000000000..d65e9473be22b91c06559b512531a195c7add5bc
--- /dev/null
+++ b/matlab/+mom/set_correct_bounds_for_stderr_corr.m
@@ -0,0 +1,43 @@
+function Bounds = set_correct_bounds_for_stderr_corr(estim_params_,Bounds)
+% function Bounds = set_correct_bounds_for_stderr_corr(estim_params_,Bounds)
+% -------------------------------------------------------------------------
+% Set correct bounds for standard deviation and corrrelation parameters
+% =========================================================================
+% INPUTS
+% o estim_params_ [struct] information on estimated parameters
+% o Bounds [struct] information on bounds
+% -------------------------------------------------------------------------
+% OUTPUT
+% o Bounds [struct] updated bounds
+% -------------------------------------------------------------------------
+% This function is called by
+% o mom.run
+% =========================================================================
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+number_of_estimated_parameters = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn+estim_params_.np;
+% set correct bounds for standard deviations and corrrelations
+param_of_interest = (1:number_of_estimated_parameters)'<=estim_params_.nvx+estim_params_.nvn;
+LB_below_0 = (Bounds.lb<0 & param_of_interest);
+Bounds.lb(LB_below_0) = 0;
+param_of_interest = (1:number_of_estimated_parameters)'> estim_params_.nvx+estim_params_.nvn & (1:number_of_estimated_parameters)'<estim_params_.nvx+estim_params_.nvn +estim_params_.ncx + estim_params_.ncn;
+LB_below_minus_1 = (Bounds.lb<-1 & param_of_interest);
+UB_above_1 = (Bounds.ub>1 & param_of_interest);
+Bounds.lb(LB_below_minus_1) = -1;
+Bounds.ub(UB_above_1) = 1;
\ No newline at end of file
diff --git a/matlab/+mom/transform_prior_to_laplace_prior.m b/matlab/+mom/transform_prior_to_laplace_prior.m
new file mode 100644
index 0000000000000000000000000000000000000000..f2cdf7cd20b644c1f4f56848f0f9f047f41fe41c
--- /dev/null
+++ b/matlab/+mom/transform_prior_to_laplace_prior.m
@@ -0,0 +1,58 @@
+function bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
+% function 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
+% -------------------------------------------------------------------------
+% OUTPUT
+% bayestopt_ [structure] Laplace prior information
+% -------------------------------------------------------------------------
+% This function is called by
+% o mom.run
+% =========================================================================
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% 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');
+ non_normal_priors = (bayestopt_.pshape~=3);
+ bayestopt_.pshape(non_normal_priors) = 3;
+ bayestopt_.p3(non_normal_priors) = -Inf*ones(sum(non_normal_priors),1);
+ bayestopt_.p4(non_normal_priors) = Inf*ones(sum(non_normal_priors),1);
+ bayestopt_.p6(non_normal_priors) = bayestopt_.p1(non_normal_priors);
+ bayestopt_.p7(non_normal_priors) = bayestopt_.p2(non_normal_priors);
+ bayestopt_.p5(non_normal_priors) = bayestopt_.p1(non_normal_priors);
+end
+if any(isinf(bayestopt_.p2)) % find infinite variance priors
+ inf_var_pars = bayestopt_.name(isinf(bayestopt_.p2));
+ disp_string = [inf_var_pars{1,:}];
+ for ii = 2:size(inf_var_pars,1)
+ disp_string = [disp_string,', ',inf_var_pars{ii,:}];
+ end
+ fprintf('The parameter(s) %s have infinite prior variance. This implies a flat prior.\n',disp_string);
+ fprintf('Dynare disables the matrix singularity warning\n');
+ if isoctave
+ warning('off','Octave:singular-matrix');
+ else
+ warning('off','MATLAB:singularMatrix');
+ end
+end
\ No newline at end of file
diff --git a/matlab/CheckPath.m b/matlab/CheckPath.m
index 794c2005bb0ab58ed87b74443c6c1fc954a17f68..f32f8fa22998e8f54b6c910beaa3195defcf0814 100644
--- a/matlab/CheckPath.m
+++ b/matlab/CheckPath.m
@@ -12,7 +12,7 @@ function [DirectoryName, info] = CheckPath(type,dname)
% SPECIAL REQUIREMENTS
% none
-% Copyright © 2005-2017 Dynare Team
+% Copyright © 2005-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -31,7 +31,7 @@ function [DirectoryName, info] = CheckPath(type,dname)
info = 0;
-DirectoryName = [ dname '/' type ];
+DirectoryName = [ dname filesep type ];
if ~isdir(dname)
% Make sure there isn't a file with the same name, see trac ticket #47
diff --git a/matlab/CutSample.m b/matlab/CutSample.m
index e29f010d5b2156eb74c8074e08700c866b3c8f32..937ff413e40545fbe99c5fe7f295f82b3828adc2 100644
--- a/matlab/CutSample.m
+++ b/matlab/CutSample.m
@@ -33,6 +33,8 @@ function CutSample(M_, options_, estim_params_)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+dispString = 'Estimation::mcmc';
+
% Get the path to the metropolis files.
MetropolisFolder = CheckPath('metropolis',M_.dname);
@@ -47,7 +49,7 @@ npar=size(record.LastParameters,2);
mh_files = dir([ MetropolisFolder ,filesep, M_.fname '_mh*.mat' ]);
if ~length(mh_files)
- error('Estimation::mcmc: I can''t find MH file to load here!')
+ error('%s: I can''t find MH file to load here!',dispString)
end
TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
@@ -71,9 +73,9 @@ end
% Save updated mh-history file.
update_last_mh_history_file(MetropolisFolder, ModelName, record);
-fprintf('Estimation::mcmc: Total number of MH draws per chain: %d.\n',TotalNumberOfMhDraws);
-fprintf('Estimation::mcmc: Total number of generated MH files: %d.\n',TotalNumberOfMhFiles);
-fprintf('Estimation::mcmc: I''ll use mh-files %d to %d.\n',FirstMhFile,TotalNumberOfMhFiles);
-fprintf('Estimation::mcmc: In MH-file number %d I''ll start at line %d.\n',FirstMhFile,FirstLine);
-fprintf('Estimation::mcmc: Finally I keep %d draws per chain.\n',TotalNumberOfMhDraws-FirstDraw+1);
+fprintf('%s: Total number of MH draws per chain: %d.\n',dispString,TotalNumberOfMhDraws);
+fprintf('%s: Total number of generated MH files: %d.\n',dispString,TotalNumberOfMhFiles);
+fprintf('%s: I''ll use mh-files %d to %d.\n',dispString,FirstMhFile,TotalNumberOfMhFiles);
+fprintf('%s: In MH-file number %d I''ll start at line %d.\n',dispString,FirstMhFile,FirstLine);
+fprintf('%s: Finally I keep %d draws per chain.\n',dispString,TotalNumberOfMhDraws-FirstDraw+1);
skipline()
\ No newline at end of file
diff --git a/matlab/PosteriorIRF.m b/matlab/PosteriorIRF.m
index 007fb89f5c3f7d18aef20cdad1ee1b3e4a2c0446..b5356fd7b18c7768f8910404f9ecf28ad6d06740 100644
--- a/matlab/PosteriorIRF.m
+++ b/matlab/PosteriorIRF.m
@@ -16,7 +16,7 @@ function PosteriorIRF(type)
% functions associated with it(the _core1 and _core2).
% See also the comments posterior_sampler.m funtion.
-% Copyright © 2006-2018 Dynare Team
+% Copyright © 2006-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -36,6 +36,8 @@ function PosteriorIRF(type)
global options_ estim_params_ oo_ M_ bayestopt_ dataset_ dataset_info
+dispString = 'Estimation::mcmc';
+
% Set the number of periods
if isempty(options_.irf) || ~options_.irf
options_.irf = 40;
@@ -287,7 +289,7 @@ if options_.TeX
end
end
-fprintf('Estimation::mcmc: Posterior (dsge) IRFs...\n');
+fprintf('%s: Posterior (dsge) IRFs...\n',dispString);
tit = M_.exo_names;
kdx = 0;
@@ -327,7 +329,7 @@ if MAX_nirfs_dsgevar
VarIRFdsgevar = zeros(options_.irf,nvar,M_.exo_nbr);
DistribIRFdsgevar = zeros(options_.irf,9,nvar,M_.exo_nbr);
HPDIRFdsgevar = zeros(options_.irf,2,nvar,M_.exo_nbr);
- fprintf('Estimation::mcmc: Posterior (bvar-dsge) IRFs...\n');
+ fprintf('%s: Posterior (bvar-dsge) IRFs...\n',dispString);
tit = M_.exo_names;
kdx = 0;
for file = 1:NumberOfIRFfiles_dsgevar
@@ -457,4 +459,4 @@ if ~options_.nograph && ~options_.no_graph.posterior
end
-fprintf('Estimation::mcmc: Posterior IRFs, done!\n');
+fprintf('%s: Posterior IRFs, done!\n',dispString);
diff --git a/matlab/check_posterior_sampler_options.m b/matlab/check_posterior_sampler_options.m
index d518abedbeb5c56e94bc7b87974a11f7c025408f..d00d1913dca838866df440e9775452c6f0bf7153 100644
--- a/matlab/check_posterior_sampler_options.m
+++ b/matlab/check_posterior_sampler_options.m
@@ -1,10 +1,11 @@
-function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, options_, bounds, bayestopt_)
-
-% function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, options_, bounds, bayestopt_)
+function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, fname, dname, options_, bounds, bayestopt_)
+% function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, fname, dname, options_, bounds, bayestopt_)
% initialization of posterior samplers
%
% INPUTS
% posterior_sampler_options: posterior sampler options
+% fname: name of the mod-file
+% dname: name of directory with metropolis folder
% options_: structure storing the options
% bounds: structure containing prior bounds
% bayestopt_: structure storing information about priors
@@ -17,7 +18,7 @@ function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sam
% SPECIAL REQUIREMENTS
% none
-% Copyright © 2015-2022 Dynare Team
+% Copyright © 2015-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -391,7 +392,7 @@ if ~strcmp(posterior_sampler_options.posterior_sampling_method,'slice')
end
if options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix
- [~, invhess] = compute_mh_covariance_matrix;
+ [~, invhess] = compute_mh_covariance_matrix(bayestopt_,fname,dname);
posterior_sampler_options.invhess = invhess;
end
@@ -413,7 +414,7 @@ if strcmp(posterior_sampler_options.posterior_sampling_method,'slice')
error('check_posterior_sampler_options:: This error should not occur, please contact developers.')
end
% % % if options_.load_mh_file && options_.use_mh_covariance_matrix,
- % % % [~, invhess] = compute_mh_covariance_matrix;
+ % % % [~, invhess] = compute_mh_covariance_matrix(bayestopt_,M_.fname,M_.dname));
% % % posterior_sampler_options.invhess = invhess;
% % % end
[V1, D]=eig(invhess);
diff --git a/matlab/compute_mh_covariance_matrix.m b/matlab/compute_mh_covariance_matrix.m
index c8f96e270c08a4748352721c1ac0f240b0cf7e43..3e1abab1568f98e339f6ca934778f5da817dbf7d 100644
--- a/matlab/compute_mh_covariance_matrix.m
+++ b/matlab/compute_mh_covariance_matrix.m
@@ -1,10 +1,13 @@
-function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix()
+function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix(bayestopt_,fname,dname)
+% function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix(bayestopt_,fname,dname)
% Estimation of the posterior covariance matrix, posterior mean, posterior mode and evaluation of the posterior kernel at the
% estimated mode, using the draws from a metropolis-hastings. The estimated posterior mode and covariance matrix are saved in
-% a file <M_.fname>_mh_mode.mat.
+% a file <fname>_mh_mode.mat.
%
% INPUTS
-% None.
+% o bayestopt_ [struct] characterizing priors
+% o fname [string] name of model
+% o dname [string] name of directory with metropolis folder
%
% OUTPUTS
% o posterior_mean [double] (n*1) vector, posterior expectation of the parameters.
@@ -31,14 +34,10 @@ function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+MetropolisFolder = CheckPath('metropolis',dname);
+BaseName = [MetropolisFolder filesep fname];
-global M_ bayestopt_
-
-MetropolisFolder = CheckPath('metropolis',M_.dname);
-ModelName = M_.fname;
-BaseName = [MetropolisFolder filesep ModelName];
-
-record=load_last_mh_history_file(MetropolisFolder, ModelName);
+record=load_last_mh_history_file(MetropolisFolder, fname);
FirstMhFile = record.KeepedDraws.FirstMhFile;
FirstLine = record.KeepedDraws.FirstLine;
@@ -71,4 +70,4 @@ hh = inv(posterior_covariance);
fval = posterior_kernel_at_the_mode;
parameter_names = bayestopt_.name;
-save([M_.dname filesep 'Output' filesep M_.fname '_mh_mode.mat'],'xparam1','hh','fval','parameter_names');
\ No newline at end of file
+save([dname filesep 'Output' filesep fname '_mh_mode.mat'],'xparam1','hh','fval','parameter_names');
\ No newline at end of file
diff --git a/matlab/convergence_diagnostics/McMCDiagnostics.m b/matlab/convergence_diagnostics/mcmc_diagnostics.m
similarity index 93%
rename from matlab/convergence_diagnostics/McMCDiagnostics.m
rename to matlab/convergence_diagnostics/mcmc_diagnostics.m
index cbe02ce8dd1b264cef5d063a6968849d33c897fd..8ad512a68cbb7f65d55d79e5dd1397855cc9775f 100644
--- a/matlab/convergence_diagnostics/McMCDiagnostics.m
+++ b/matlab/convergence_diagnostics/mcmc_diagnostics.m
@@ -1,5 +1,5 @@
-function oo_ = McMCDiagnostics(options_, estim_params_, M_, oo_)
-% function oo_ = McMCDiagnostics(options_, estim_params_, M_, oo_)
+function oo_ = mcmc_diagnostics(options_, estim_params_, M_, oo_)
+% function oo_ = mcmc_diagnostics(options_, estim_params_, M_, oo_)
% Computes convergence tests
%
% INPUTS
@@ -51,11 +51,11 @@ for b = 1:nblck
nfiles = length(dir([MetropolisFolder ,filesep, ModelName '_mh*_blck' num2str(b) '.mat']));
if ~isequal(NumberOfMcFilesPerBlock,nfiles)
issue_an_error_message = 1;
- disp(['Estimation::mcmc::diagnostics: The number of MCMC files in chain ' num2str(b) ' is ' num2str(nfiles) ' while the mh-history files indicate that we should have ' num2str(NumberOfMcFilesPerBlock) ' MCMC files per chain!'])
+ fprintf('The number of MCMC files in chain %u is %u while the mh-history files indicate that we should have %u MCMC files per chain!\n',b, nfiles, NumberOfMcFilesPerBlock);
end
end
if issue_an_error_message
- error('Estimation::mcmc::diagnostics: I cannot proceed because some MCMC files are missing. Check your MCMC files...')
+ error('mcmc_diagnostics: I cannot proceed because some MCMC files are missing. Check your MCMC files...!');
end
% compute inefficiency factor
@@ -111,7 +111,7 @@ PastDraws = sum(record.MhDraws,1);
NumberOfDraws = PastDraws(1);
if NumberOfDraws<=2000
- warning(['estimation:: MCMC convergence diagnostics are not computed because the total number of iterations is not bigger than 2000!'])
+ warning('MCMC convergence diagnostics are not computed because the total number of iterations is not bigger than 2000!');
return
end
@@ -185,7 +185,7 @@ if nblck == 1 % Brooks and Gelman tests need more than one block
if options_.convergence.rafterylewis.indicator
if any(options_.convergence.rafterylewis.qrs<0) || any(options_.convergence.rafterylewis.qrs>1) || length(options_.convergence.rafterylewis.qrs)~=3 ...
|| (options_.convergence.rafterylewis.qrs(1)-options_.convergence.rafterylewis.qrs(2)<=0)
- fprintf('\nCONVERGENCE DIAGNOSTICS: Invalid option for raftery_lewis_qrs. Using the default of [0.025 0.005 0.95].\n')
+ fprintf('\nInvalid option for raftery_lewis_qrs. Using the default of [0.025 0.005 0.95].\n');
options_.convergence.rafterylewis.qrs=[0.025 0.005 0.95];
end
Raftery_Lewis_q=options_.convergence.rafterylewis.qrs(1);
@@ -218,18 +218,18 @@ xx = Origin:StepSize:NumberOfDraws;
NumberOfLines = length(xx);
if NumberOfDraws < Origin
- disp('Estimation::mcmc::diagnostics: The number of simulations is too small to compute the MCMC convergence diagnostics.')
+ fprintf('The number of simulations is too small to compute the MCMC convergence diagnostics.\n');
return
end
if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([OutputFolder '/' ModelName '_UnivariateDiagnostics.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by McmcDiagnostics.m (Dynare).\n');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by mcmc_diagnostics.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
end
-disp('Estimation::mcmc::diagnostics: Univariate convergence diagnostic, Brooks and Gelman (1998):')
+fprintf('Univariate convergence diagnostic, Brooks and Gelman (1998):\n');
% The mandatory variables for local/remote parallel
% computing are stored in localVars struct.
@@ -248,7 +248,7 @@ localVars.M_ = M_;
% Like sequential execution!
if isnumeric(options_.parallel)
- fout = McMCDiagnostics_core(localVars,1,npar,0);
+ fout = mcmc_diagnostics_core(localVars,1,npar,0);
UDIAG = fout.UDIAG;
clear fout
% Parallel execution!
@@ -258,7 +258,7 @@ else
end
NamFileInput={[M_.dname '/metropolis/'],[ModelName '_mh*_blck*.mat']};
- [fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, 1, npar,NamFileInput,'McMCDiagnostics_core', localVars, [], options_.parallel_info);
+ [fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, 1, npar,NamFileInput,'mcmc_diagnostics_core', localVars, [], options_.parallel_info);
UDIAG = fout(1).UDIAG;
for j=2:totCPU
UDIAG = cat(3,UDIAG ,fout(j).UDIAG);
@@ -397,7 +397,7 @@ clear UDIAG;
%
if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([OutputFolder '/' ModelName '_MultivariateDiagnostics.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by McmcDiagnostics.m (Dynare).\n');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by mcmc_diagnostics.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
end
diff --git a/matlab/convergence_diagnostics/McMCDiagnostics_core.m b/matlab/convergence_diagnostics/mcmc_diagnostics_core.m
similarity index 93%
rename from matlab/convergence_diagnostics/McMCDiagnostics_core.m
rename to matlab/convergence_diagnostics/mcmc_diagnostics_core.m
index 2cb710f645071f737751d93ca6d5f01062d54e65..a9ab4581e43543dce08ef14f6fb5e87761cbbca4 100644
--- a/matlab/convergence_diagnostics/McMCDiagnostics_core.m
+++ b/matlab/convergence_diagnostics/mcmc_diagnostics_core.m
@@ -1,5 +1,5 @@
-function myoutput = McMCDiagnostics_core(myinputs,fpar,npar,whoiam, ThisMatlab)
-% function myoutput = McMCDiagnostics_core(myinputs,fpar,npar,whoiam, ThisMatlab)
+function myoutput = mcmc_diagnostics_core(myinputs,fpar,npar,whoiam, ThisMatlab)
+% function myoutput = mcmc_diagnostics_core(myinputs,fpar,npar,whoiam, ThisMatlab)
% Computes the Brooks/Gelman (1998) convergence diagnostics, both the
% parameteric and the non-parameteric versions
%
@@ -20,11 +20,10 @@ function myoutput = McMCDiagnostics_core(myinputs,fpar,npar,whoiam, ThisMatlab)
% 4nd column: sum of within sequence variances; used to compute mean within sequence variances
% 5nd column: within sequence kurtosis
% 6nd column: sum of within sequence kurtoses; used to compute mean within sequence kurtoses
-% Averaging to compute mean moments is done in
-% McMCDiagnostics
+% Averaging to compute mean moments is done in mcmc_diagnostics
%
% ALGORITHM
-% Computes part of the convergence diagnostics, the rest is computed in McMCDiagnostics.m .
+% Computes part of the convergence diagnostics, the rest is computed in mcmc_diagnostics.m.
% The methodology and terminology is based on: Brooks/Gelman (1998): General
% Methods for Monitoring Convergence of Iterative Simulations, Journal of Computational
% and Graphical Statistics, Volume 7, Number 4, Pages 434-455
@@ -33,7 +32,7 @@ function myoutput = McMCDiagnostics_core(myinputs,fpar,npar,whoiam, ThisMatlab)
% SPECIAL REQUIREMENTS.
% None.
-% Copyright © 2006-2017 Dynare Team
+% Copyright © 2006-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -80,7 +79,7 @@ tmp = zeros(NumberOfDraws*nblck,3);
UDIAG = zeros(NumberOfLines,6,npar-fpar+1);
if whoiam
- waitbarString = ['Please wait... McMCDiagnostics (' int2str(fpar) 'of' int2str(npar) ')...'];
+ waitbarString = ['Please wait... MCMC diagnostics (' int2str(fpar) 'of' int2str(npar) ')...'];
if Parallel(ThisMatlab).Local
waitbarTitle=['Local '];
else
diff --git a/matlab/display_estimation_results_table.m b/matlab/display_estimation_results_table.m
index 92c8a53486289d8bed5d7c91c7ea39f26ddfa14b..4d0b7031a45460e27b5384d4456cae99d6e496ba 100644
--- a/matlab/display_estimation_results_table.m
+++ b/matlab/display_estimation_results_table.m
@@ -174,7 +174,7 @@ if ncn
end
if any(xparam1(1:nvx+nvn)<0)
- warning('Some estimated standard deviations are negative. Dynare internally works with variances so that the sign does not matter. Nevertheless, it is recommended to impose either prior restrictions (Bayesian Estimation) or a lower bound (ML) to assure positive values.')
+ warning(sprintf('Some estimated standard deviations are negative.\n Dynare internally works with variances so that the sign does not matter.\n Nevertheless, it is recommended to impose either prior restrictions (Bayesian Estimation)\n or a lower bound (ML) to assure positive values.'))
end
OutputDirectoryName = CheckPath('Output',M_.dname);
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 8bb1337aeeac801bcbc98683ef86fcac38a202a9..29aa7842defc51caf0c3b960c41cf129ce8d12e2 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -12,7 +12,7 @@ function dynare_estimation_1(var_list_,dname)
% SPECIAL REQUIREMENTS
% none
-% Copyright © 2003-2022 Dynare Team
+% Copyright © 2003-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -31,6 +31,8 @@ function dynare_estimation_1(var_list_,dname)
global M_ options_ oo_ estim_params_ bayestopt_ dataset_ dataset_info
+dispString = 'Estimation::mcmc';
+
if ~exist([M_.dname filesep 'Output'],'dir')
if isoctave && octave_ver_less_than('7') && ~exist(M_.dname)
% See https://savannah.gnu.org/bugs/index.php?61166
@@ -293,37 +295,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
if ~options_.mh_posterior_mode_estimation && options_.cova_compute
- try
- chol(hh);
- catch
- skipline()
- disp('POSTERIOR KERNEL OPTIMIZATION PROBLEM!')
- disp(' (minus) the hessian matrix at the "mode" is not positive definite!')
- disp('=> posterior variance of the estimated parameters are not positive.')
- disp('You should try to change the initial values of the parameters using')
- disp('the estimated_params_init block, or use another optimization routine.')
- params_at_bound=find(abs(xparam1-bounds.ub)<1.e-10 | abs(xparam1-bounds.lb)<1.e-10);
- if ~isempty(params_at_bound)
- for ii=1:length(params_at_bound)
- params_at_bound_name{ii,1}=get_the_name(params_at_bound(ii),0,M_,estim_params_,options_);
- end
- disp_string=[params_at_bound_name{1,:}];
- for ii=2:size(params_at_bound_name,1)
- disp_string=[disp_string,', ',params_at_bound_name{ii,:}];
- end
- fprintf('\nThe following parameters are at the prior bound: %s\n', disp_string)
- fprintf('Some potential solutions are:\n')
- fprintf(' - Check your model for mistakes.\n')
- fprintf(' - Check whether model and data are consistent (correct observation equation).\n')
- fprintf(' - Shut off prior_trunc.\n')
- fprintf(' - Change the optimization bounds.\n')
- fprintf(' - Use a different mode_compute like 6 or 9.\n')
- fprintf(' - Check whether the parameters estimated are identified.\n')
- fprintf(' - Check prior shape (e.g. Inf density at bound(s)).\n')
- fprintf(' - Increase the informativeness of the prior.\n')
- end
- warning('The results below are most likely wrong!');
- end
+ check_hessian_at_the_mode(hh, xparam1, M_, estim_params_, options_, bounds);
end
if options_.mode_check.status && ~options_.mh_posterior_mode_estimation
@@ -404,73 +376,19 @@ if np > 0
save([M_.dname filesep 'Output' filesep M_.fname '_params.mat'],'pindx');
end
-switch options_.MCMC_jumping_covariance
- case 'hessian' %Baseline
- %do nothing and use hessian from mode_compute
- case 'prior_variance' %Use prior variance
- if any(isinf(bayestopt_.p2))
- error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
- else
- hh = diag(1./(bayestopt_.p2.^2));
- end
- hsd = sqrt(diag(hh));
- invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
- case 'identity_matrix' %Use identity
- invhess = eye(nx);
- otherwise %user specified matrix in file
- try
- load(options_.MCMC_jumping_covariance,'jumping_covariance')
- hh=jumping_covariance;
- catch
- error(['No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat'])
- end
- [nrow, ncol]=size(hh);
- if ~isequal(nrow,ncol) && ~isequal(nrow,nx) %check if square and right size
- error(['jumping_covariance matrix must be square and have ',num2str(nx),' rows and columns'])
- end
- try %check for positive definiteness
- chol(hh);
- hsd = sqrt(diag(hh));
- invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
- catch
- error(['Specified jumping_covariance is not positive definite'])
- end
-end
+invhess = set_mcmc_jumping_covariance(invhess, nx, options_.MCMC_jumping_covariance, bayestopt_, 'dynare_estimation_1');
if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
(any(bayestopt_.pshape >0 ) && options_.load_mh_file) %% not ML estimation
- bounds = prior_bounds(bayestopt_, options_.prior_trunc); %reset bounds as lb and ub must only be operational during mode-finding
- outside_bound_pars=find(xparam1 < bounds.lb | xparam1 > bounds.ub);
- if ~isempty(outside_bound_pars)
- for ii=1:length(outside_bound_pars)
- outside_bound_par_names{ii,1}=get_the_name(ii,0,M_,estim_params_,options_);
- end
- disp_string=[outside_bound_par_names{1,:}];
- for ii=2:size(outside_bound_par_names,1)
- disp_string=[disp_string,', ',outside_bound_par_names{ii,:}];
- end
- if options_.prior_trunc>0
- error(['Estimation:: Mode value(s) of ', disp_string ,' are outside parameter bounds. Potentially, you should set prior_trunc=0.'])
- else
- error(['Estimation:: Mode value(s) of ', disp_string ,' are outside parameter bounds.'])
- end
- end
+ %reset bounds as lb and ub must only be operational during mode-finding
+ bounds = set_mcmc_prior_bounds(xparam1, bayestopt_, options_, 'dynare_estimation_1');
% Tunes the jumping distribution's scale parameter
if options_.mh_tune_jscale.status
if strcmp(options_.posterior_sampler_options.posterior_sampling_method, 'random_walk_metropolis_hastings')
- %get invhess in case of use_mh_covariance_matrix
- posterior_sampler_options_temp = options_.posterior_sampler_options.current_options;
- posterior_sampler_options_temp.invhess = invhess;
- posterior_sampler_options_temp = check_posterior_sampler_options(posterior_sampler_options_temp, options_);
-
- options = options_.mh_tune_jscale;
- options.rwmh = options_.posterior_sampler_options.rwmh;
- options_.mh_jscale = calibrate_mh_scale_parameter(objective_function, ...
- posterior_sampler_options_temp.invhess, xparam1, [bounds.lb,bounds.ub], ...
- options, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_);
- clear('posterior_sampler_options_temp','options')
+ options_.mh_jscale = tune_mcmc_mh_jscale_wrapper(invhess, options_, M_, objective_function, xparam1, bounds,...
+ dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_);
bayestopt_.jscale(:) = options_.mh_jscale;
- fprintf('mh_jscale has been set equal to %s\n', num2str(options_.mh_jscale))
+ fprintf('mh_jscale has been set equal to %s\n', num2str(options_.mh_jscale));
else
warning('mh_tune_jscale is only available with Random Walk Metropolis Hastings!')
end
@@ -479,7 +397,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
if options_.mh_replic || options_.load_mh_file
posterior_sampler_options = options_.posterior_sampler_options.current_options;
posterior_sampler_options.invhess = invhess;
- [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, options_, bounds, bayestopt_);
+ [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, M_.fname, M_.dname, options_, bounds, bayestopt_);
% store current options in global
options_.posterior_sampler_options.current_options = posterior_sampler_options;
if options_.mh_replic
@@ -492,7 +410,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
%% Here I discard first mh_drop percent of the draws:
CutSample(M_, options_, estim_params_);
if options_.mh_posterior_mode_estimation
- [~,~,posterior_mode,~] = compute_mh_covariance_matrix();
+ [~,~,posterior_mode,~] = compute_mh_covariance_matrix(bayestopt_,M_.fname,M_.dname);
oo_=fill_mh_mode(posterior_mode',NaN(length(posterior_mode),1),M_,options_,estim_params_,bayestopt_,oo_,'posterior');
%reset qz_criterium
options_.qz_criterium=qz_criterium_old;
@@ -504,7 +422,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
end
if ~options_.nodiagnostic
if (options_.mh_replic>0 || (options_.load_mh_file && ~options_.load_results_after_load_mh))
- oo_= McMCDiagnostics(options_, estim_params_, M_,oo_);
+ oo_= mcmc_diagnostics(options_, estim_params_, M_,oo_);
elseif options_.load_mh_file && options_.load_results_after_load_mh
if isfield(oo_load_mh.oo_,'convergence')
oo_.convergence=oo_load_mh.oo_.convergence;
@@ -547,13 +465,13 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
if ~(~isempty(options_.sub_draws) && options_.sub_draws==0)
if options_.bayesian_irf
if error_flag
- error('Estimation::mcmc: I cannot compute the posterior IRFs!')
+ error('%s: I cannot compute the posterior IRFs!',dispString)
end
PosteriorIRF('posterior');
end
if options_.moments_varendo
if error_flag
- error('Estimation::mcmc: I cannot compute the posterior moments for the endogenous variables!')
+ error('%s: I cannot compute the posterior moments for the endogenous variables!',dispString)
end
if options_.load_mh_file && options_.mh_replic==0 %user wants to recompute results
[MetropolisFolder, info] = CheckPath('metropolis',M_.dname);
@@ -578,16 +496,16 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
end
if options_.smoother || ~isempty(options_.filter_step_ahead) || options_.forecast
if error_flag
- error('Estimation::mcmc: I cannot compute the posterior statistics!')
+ error('%s: I cannot compute the posterior statistics!',dispString)
end
if options_.order==1 && ~options_.particle.status
prior_posterior_statistics('posterior',dataset_,dataset_info); %get smoothed and filtered objects and forecasts
else
- error('Estimation::mcmc: Particle Smoothers are not yet implemented.')
+ error('%s: Particle Smoothers are not yet implemented.',dispString)
end
end
else
- fprintf('Estimation:mcmc: sub_draws was set to 0. Skipping posterior computations.')
+ fprintf('%s: sub_draws was set to 0. Skipping posterior computations.',dispString);
end
xparam1 = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
M_ = set_all_parameters(xparam1,estim_params_,M_);
diff --git a/matlab/dynare_estimation_init.m b/matlab/dynare_estimation_init.m
index a81627bc2d21a3f6b411151a447d693ae9022961..179d39f5019c7a207e76fb23559826ccc90fae57 100644
--- a/matlab/dynare_estimation_init.m
+++ b/matlab/dynare_estimation_init.m
@@ -80,26 +80,11 @@ if ~isfield(options_,'varobs')
error('VAROBS statement is missing!')
end
+% Checks on VAROBS
+check_varobs_are_endo_and_declared_once(options_.varobs,M_.endo_names);
% Set the number of observed variables.
options_.number_of_observed_variables = length(options_.varobs);
-% Check that each declared observed variable is also an endogenous variable.
-for i = 1:options_.number_of_observed_variables
- id = strmatch(options_.varobs{i}, M_.endo_names, 'exact');
- if isempty(id)
- error(['Unknown variable (' options_.varobs{i} ')!'])
- end
-end
-
-% Check that a variable is not declared as observed more than once.
-if length(unique(options_.varobs))<length(options_.varobs)
- for i = 1:options_.number_of_observed_variables
- if length(strmatch(options_.varobs{i},options_.varobs,'exact'))>1
- error(['A variable cannot be declared as observed more than once (' options_.varobs{i} ')!'])
- end
- end
-end
-
if options_.discretionary_policy
if options_.order>1
error('discretionary_policy does not support order>1');
@@ -173,133 +158,7 @@ end
% Check that the provided mode_file is compatible with the current estimation settings.
if ~isempty(estim_params_) && ~(isfield(estim_params_,'nvx') && sum(estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn+estim_params_.np)==0) && ~isempty(options_.mode_file) && ~options_.mh_posterior_mode_estimation
- number_of_estimated_parameters = length(xparam1);
- mode_file = load(options_.mode_file);
- if number_of_estimated_parameters>length(mode_file.xparam1)
- % More estimated parameters than parameters in the mode file.
- skipline()
- disp(['The posterior mode file ' options_.mode_file ' has been generated using another specification of the model or another model!'])
- disp(['Your mode file contains estimates for ' int2str(length(mode_file.xparam1)) ' parameters, while you are attempting to estimate ' int2str(number_of_estimated_parameters) ' parameters:'])
- md = []; xd = [];
- for i=1:number_of_estimated_parameters
- id = strmatch(deblank(bayestopt_.name(i,:)),mode_file.parameter_names,'exact');
- if isempty(id)
- disp(['--> Estimated parameter ' bayestopt_.name{i} ' is not present in the loaded mode file (prior mean will be used, if possible).'])
- else
- xd = [xd; i];
- md = [md; id];
- end
- end
- for i=1:length(mode_file.xparam1)
- id = strmatch(mode_file.parameter_names{i},bayestopt_.name,'exact');
- if isempty(id)
- disp(['--> Parameter ' mode_file.parameter_names{i} ' is not estimated according to the current mod file.'])
- end
- end
- if ~options_.mode_compute
- % The posterior mode is not estimated.
- error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
- else
- % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
- if ~isempty(xd)
- xparam1(xd) = mode_file.xparam1(md);
- else
- error('Please remove the mode_file option.')
- end
- end
- elseif number_of_estimated_parameters<length(mode_file.xparam1)
- % Less estimated parameters than parameters in the mode file.
- skipline()
- disp(['The posterior mode file ' options_.mode_file ' has been generated using another specification of the model or another model!'])
- disp(['Your mode file contains estimates for ' int2str(length(mode_file.xparam1)) ' parameters, while you are attempting to estimate only ' int2str(number_of_estimated_parameters) ' parameters:'])
- md = []; xd = [];
- for i=1:number_of_estimated_parameters
- id = strmatch(deblank(bayestopt_.name(i,:)),mode_file.parameter_names,'exact');
- if isempty(id)
- disp(['--> Estimated parameter ' deblank(bayestopt_.name(i,:)) ' is not present in the loaded mode file (prior mean will be used, if possible).'])
- else
- xd = [xd; i];
- md = [md; id];
- end
- end
- for i=1:length(mode_file.xparam1)
- id = strmatch(mode_file.parameter_names{i},bayestopt_.name,'exact');
- if isempty(id)
- disp(['--> Parameter ' mode_file.parameter_names{i} ' is not estimated according to the current mod file.'])
- end
- end
- if ~options_.mode_compute
- % The posterior mode is not estimated. If possible, fix the mode_file.
- if isequal(length(xd),number_of_estimated_parameters)
- disp('==> Fix mode file (remove unused parameters).')
- xparam1 = mode_file.xparam1(md);
- if isfield(mode_file,'hh')
- hh = mode_file.hh(md,md);
- end
- else
- error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
- end
- else
- % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
- if ~isempty(xd)
- xparam1(xd) = mode_file.xparam1(md);
- else
- % None of the estimated parameters are present in the mode_file.
- error('Please remove the mode_file option.')
- end
- end
- else
- % The number of declared estimated parameters match the number of parameters in the mode file.
- % Check that the parameters in the mode file and according to the current mod file are identical.
- if ~isfield(mode_file,'parameter_names')
- disp(['The posterior mode file ' options_.mode_file ' has been generated using an older version of Dynare. It cannot be verified if it matches the present model. Proceed at your own risk.'])
- mode_file.parameter_names=deblank(bayestopt_.name); %set names
- end
- if isequal(mode_file.parameter_names, bayestopt_.name)
- xparam1 = mode_file.xparam1;
- if isfield(mode_file,'hh')
- hh = mode_file.hh;
- end
- else
- skipline()
- disp(['The posterior mode file ' options_.mode_file ' has been generated using another specification of the model or another model!'])
- % Check if this only an ordering issue or if the missing parameters can be initialized with the prior mean.
- md = []; xd = [];
- for i=1:number_of_estimated_parameters
- id = strmatch(deblank(bayestopt_.name(i,:)), mode_file.parameter_names,'exact');
- if isempty(id)
- disp(['--> Estimated parameter ' bayestopt_.name{i} ' is not present in the loaded mode file.'])
- else
- xd = [xd; i];
- md = [md; id];
- end
- end
- if ~options_.mode_compute
- % The posterior mode is not estimated
- if isequal(length(xd), number_of_estimated_parameters)
- % This is an ordering issue.
- xparam1 = mode_file.xparam1(md);
- if isfield(mode_file,'hh')
- hh = mode_file.hh(md,md);
- end
- else
- error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
- end
- else
- % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
- if ~isempty(xd)
- xparam1(xd) = mode_file.xparam1(md);
- if isfield(mode_file,'hh')
- hh(xd,xd) = mode_file.hh(md,md);
- end
- else
- % None of the estimated parameters are present in the mode_file.
- error('Please remove the mode_file option.')
- end
- end
- end
- end
- skipline()
+ [xparam1, hh] = check_mode_file(xparam1, hh, options_, bayestopt_);
end
%check for calibrated covariances before updating parameters
@@ -627,7 +486,7 @@ if options_.load_results_after_load_mh
end
if options_.mh_replic || options_.load_mh_file
- [current_options, options_, bayestopt_] = check_posterior_sampler_options([], options_, bounds, bayestopt_);
+ [current_options, options_, bayestopt_] = check_posterior_sampler_options([], M_.fname, M_.dname, options_, bounds, bayestopt_);
options_.posterior_sampler_options.current_options = current_options;
end
diff --git a/matlab/get_the_name.m b/matlab/get_the_name.m
index dfeb7c909c2d854a0ff403cda0da1286a6a82fd9..11de078cf9ad9bd26c8b8d04a369e77fbd58e657 100644
--- a/matlab/get_the_name.m
+++ b/matlab/get_the_name.m
@@ -32,7 +32,7 @@ function [nam, texnam] = get_the_name(k, TeX, M_, estim_params_, options_)
%! @sp 2
%! @strong{This function is called by:}
%! @sp 1
-%! @ref{get_prior_info}, @ref{McMCDiagnostics}, @ref{mode_check}, @ref{PlotPosteriorDistributions}, @ref{plot_priors}
+%! @ref{get_prior_info}, @ref{mcmc_diagnostics}, @ref{mode_check}, @ref{PlotPosteriorDistributions}, @ref{plot_priors}
%! @sp 2
%! @strong{This function calls:}
%! @sp 1
diff --git a/matlab/initial_estimation_checks.m b/matlab/initial_estimation_checks.m
index 47adc09aa4efc12ec66980e59ddb4dbcb54fe7f6..1340f7e5e21d882810bed9cf658bdccefb3d0872 100644
--- a/matlab/initial_estimation_checks.m
+++ b/matlab/initial_estimation_checks.m
@@ -161,49 +161,12 @@ if (any(BayesInfo.pshape >0 ) && DynareOptions.mh_replic) && DynareOptions.mh_n
error(['initial_estimation_checks:: Bayesian estimation cannot be conducted with mh_nblocks=0.'])
end
-old_steady_params=Model.params; %save initial parameters for check if steady state changes param values
+% check and display warnings if steady-state solves static model (except if diffuse_filter == 1) and if steady-state changes estimated parameters
+[DynareResults.steady_state] = check_steady_state_changes_parameters(Model,EstimatedParameters,DynareResults,DynareOptions, [DynareOptions.diffuse_filter==0 DynareOptions.diffuse_filter==0] );
-% % check if steady state solves static model (except if diffuse_filter == 1)
-[DynareResults.steady_state, new_steady_params] = evaluate_steady_state(DynareResults.steady_state,[DynareResults.exo_steady_state; DynareResults.exo_det_steady_state],Model,DynareOptions,DynareOptions.diffuse_filter==0);
-
-if isfield(EstimatedParameters,'param_vals') && ~isempty(EstimatedParameters.param_vals)
- %check whether steady state file changes estimated parameters
- Model_par_varied=Model; %store Model structure
- Model_par_varied.params(EstimatedParameters.param_vals(:,1))=Model_par_varied.params(EstimatedParameters.param_vals(:,1))*1.01; %vary parameters
- [~, new_steady_params_2] = evaluate_steady_state(DynareResults.steady_state,[DynareResults.exo_steady_state; DynareResults.exo_det_steady_state],Model_par_varied,DynareOptions,DynareOptions.diffuse_filter==0);
-
- changed_par_indices=find((old_steady_params(EstimatedParameters.param_vals(:,1))-new_steady_params(EstimatedParameters.param_vals(:,1))) ...
- | (Model_par_varied.params(EstimatedParameters.param_vals(:,1))-new_steady_params_2(EstimatedParameters.param_vals(:,1))));
-
- if ~isempty(changed_par_indices)
- fprintf('\nThe steady state file internally changed the values of the following estimated parameters:\n')
- disp(char(Model.param_names(EstimatedParameters.param_vals(changed_par_indices,1))))
- fprintf('This will override the parameter values drawn from the proposal density and may lead to wrong results.\n')
- fprintf('Check whether this is really intended.\n')
- warning('The steady state file internally changes the values of the estimated parameters.')
- end
-end
-
-if any(BayesInfo.pshape) % if Bayesian estimation
- nvx=EstimatedParameters.nvx;
- if nvx && any(BayesInfo.p3(1:nvx)<0)
- warning('Your prior allows for negative standard deviations for structural shocks. Due to working with variances, Dynare will be able to continue, but it is recommended to change your prior.')
- end
- offset=nvx;
- nvn=EstimatedParameters.nvn;
- if nvn && any(BayesInfo.p3(1+offset:offset+nvn)<0)
- warning('Your prior allows for negative standard deviations for measurement error. Due to working with variances, Dynare will be able to continue, but it is recommended to change your prior.')
- end
- offset = nvx+nvn;
- ncx=EstimatedParameters.ncx;
- if ncx && (any(BayesInfo.p3(1+offset:offset+ncx)<-1) || any(BayesInfo.p4(1+offset:offset+ncx)>1))
- warning('Your prior allows for correlations between structural shocks larger than +-1 and will not integrate to 1 due to truncation. Please change your prior')
- end
- offset = nvx+nvn+ncx;
- ncn=EstimatedParameters.ncn;
- if ncn && (any(BayesInfo.p3(1+offset:offset+ncn)<-1) || any(BayesInfo.p4(1+offset:offset+ncn)>1))
- warning('Your prior allows for correlations between measurement errors larger than +-1 and will not integrate to 1 due to truncation. Please change your prior')
- end
+% check and display warning if negative values of stderr or corr params are outside unit circle for Bayesian estimation
+if any(BayesInfo.pshape)
+ check_prior_stderr_corr(EstimatedParameters,BayesInfo);
end
% display warning if some parameters are still NaN
diff --git a/matlab/marginal_density.m b/matlab/marginal_density.m
index 48014ba616fbc98a967396edb9be0e507e8d35de..2f8c45bd76d9565c84d26a57ec804fc0dd9d253e 100644
--- a/matlab/marginal_density.m
+++ b/matlab/marginal_density.m
@@ -48,7 +48,7 @@ TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
TODROP = floor(options_.mh_drop*TotalNumberOfMhDraws);
fprintf('Estimation::marginal density: I''m computing the posterior mean and covariance... ');
-[posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix();
+[posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix(bayestopt_,M_.fname,M_.dname);
MU = transpose(posterior_mean);
SIGMA = posterior_covariance;
diff --git a/matlab/metropolis_draw.m b/matlab/metropolis_draw.m
index 690dcac5d7628833b55fb5236a128d18ed985501..505d1dbeb6b5deaa7964a366b882f5fabf2cd2a7 100644
--- a/matlab/metropolis_draw.m
+++ b/matlab/metropolis_draw.m
@@ -72,8 +72,8 @@ if init
else
if options_.sub_draws>NumberOfDraws*mh_nblck
skipline()
- disp(['Estimation::mcmc: The value of option sub_draws (' num2str(options_.sub_draws) ') is greater than the number of available draws in the MCMC (' num2str(NumberOfDraws*mh_nblck) ')!'])
- disp('Estimation::mcmc: You can either change the value of sub_draws, reduce the value of mh_drop, or run another mcmc (with the load_mh_file option).')
+ disp(['The value of option sub_draws (' num2str(options_.sub_draws) ') is greater than the number of available draws in the MCMC (' num2str(NumberOfDraws*mh_nblck) ')!'])
+ disp('You can either change the value of sub_draws, reduce the value of mh_drop, or run another mcmc (with the load_mh_file option).')
skipline()
xparams = 1; % xparams is interpreted as an error flag
end
diff --git a/matlab/pm3.m b/matlab/pm3.m
index c2d19f0921a2045732235de22f339693f14748da..658a58eb71361aea98f516875babd2576c735ac1 100644
--- a/matlab/pm3.m
+++ b/matlab/pm3.m
@@ -24,7 +24,7 @@ function pm3(n1,n2,ifil,B,tit1,tit2,tit3,tit_tex,names1,names2,name3,DirectoryNa
% See also the comment in posterior_sampler.m funtion.
-% Copyright © 2007-2018 Dynare Team
+% Copyright © 2007-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -43,6 +43,8 @@ function pm3(n1,n2,ifil,B,tit1,tit2,tit3,tit_tex,names1,names2,name3,DirectoryNa
global options_ M_ oo_
+dispString = 'Estimation::mcmc';
+
nn = 3;
MaxNumberOfPlotsPerFigure = nn^2; % must be square
varlist = names2;
@@ -76,7 +78,7 @@ HPD = zeros(2,n2,nvar);
if options_.estimation.moments_posterior_density.indicator
Density = zeros(options_.estimation.moments_posterior_density.gridpoints,2,n2,nvar);
end
-fprintf(['Estimation::mcmc: ' tit1 '\n']);
+fprintf(['%s: ' tit1 '\n'],dispString);
k = 0;
filter_step_ahead_indicator=0;
filter_covar_indicator=0;
@@ -161,7 +163,7 @@ elseif filter_covar_indicator
oo_.FilterCovariance.post_deciles=post_deciles;
oo_.FilterCovariance.HPDinf=squeeze(hpd_interval(:,:,:,1));
oo_.FilterCovariance.HPDsup=squeeze(hpd_interval(:,:,:,2));
- fprintf(['Estimation::mcmc: ' tit1 ', done!\n']);
+ fprintf(['%s: ' tit1 ', done!\n'],dispString);
return
elseif state_uncert_indicator
draw_dimension=4;
@@ -183,7 +185,7 @@ elseif state_uncert_indicator
oo_.Smoother.State_uncertainty.post_deciles=post_deciles;
oo_.Smoother.State_uncertainty.HPDinf=squeeze(hpd_interval(:,:,:,1));
oo_.Smoother.State_uncertainty.HPDsup=squeeze(hpd_interval(:,:,:,2));
- fprintf(['Estimation::mcmc: ' tit1 ', done!\n']);
+ fprintf(['%s: ' tit1 ', done!\n'],dispString);
return
end
@@ -280,7 +282,7 @@ else
end
if strcmp(var_type,'_trend_coeff') || max(max(abs(Mean(:,:))))<=10^(-6) || all(all(isnan(Mean)))
- fprintf(['Estimation::mcmc: ' tit1 ', done!\n']);
+ fprintf(['%s: ' tit1 ', done!\n'],dispString);
return %not do plots
end
%%
@@ -378,4 +380,4 @@ if ~options_.nograph && ~options_.no_graph.posterior
end
end
-fprintf(['Estimation::mcmc: ' tit1 ', done!\n']);
+fprintf(['%s: ' tit1 ', done!\n'],dispString);
diff --git a/matlab/posterior_sampler.m b/matlab/posterior_sampler.m
index fbc395895b94b2fc1c5e62e66afc5da2838b78c5..8a98bfc24dce99e591c9d38b5c76526d7d8fc538 100644
--- a/matlab/posterior_sampler.m
+++ b/matlab/posterior_sampler.m
@@ -53,6 +53,8 @@ function posterior_sampler(TargetFun,ProposalFun,xparam1,sampler_options,mh_boun
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+dispString = 'Estimation::mcmc';
+
vv = sampler_options.invhess;
% Initialization of the sampler
[ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d, bayestopt_] = ...
@@ -173,10 +175,10 @@ update_last_mh_history_file(MetropolisFolder, ModelName, record);
% Provide diagnostic output
skipline()
-disp(['Estimation::mcmc: Number of mh files: ' int2str(NewFile(1)) ' per block.'])
-disp(['Estimation::mcmc: Total number of generated files: ' int2str(NewFile(1)*nblck) '.'])
-disp(['Estimation::mcmc: Total number of iterations: ' int2str((NewFile(1)-1)*MAX_nruns+irun-1) '.'])
-disp(['Estimation::mcmc: Current acceptance ratio per chain: '])
+fprintf('%s: Number of mh files: %u per block.\n', dispString, NewFile(1));
+fprintf('%s: Total number of generated files: %u.\n', dispString, NewFile(1)*nblck);
+fprintf('%s: Total number of iterations: %u.\n', dispString, (NewFile(1)-1)*MAX_nruns+irun-1);
+fprintf('%s: Current acceptance ratio per chain:\n', dispString);
for i=1:nblck
if i<10
disp([' Chain ' num2str(i) ': ' num2str(100*record.AcceptanceRatio(i)) '%'])
@@ -185,7 +187,7 @@ for i=1:nblck
end
end
if max(record.FunctionEvalPerIteration)>1
- disp(['Estimation::mcmc: Current function evaluations per iteration: '])
+ fprintf('%s: Current function evaluations per iteration:\n', dispString);
for i=1:nblck
if i<10
disp([' Chain ' num2str(i) ': ' num2str(record.FunctionEvalPerIteration(i))])
diff --git a/matlab/posterior_sampler_initialization.m b/matlab/posterior_sampler_initialization.m
index 2ac7f6963bc4257be94fc81683f2fb4ba00c8e14..36c3087771699a71dd3a65fc208e046f051ac533 100644
--- a/matlab/posterior_sampler_initialization.m
+++ b/matlab/posterior_sampler_initialization.m
@@ -55,6 +55,8 @@ function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npa
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+dispString = 'Estimation::mcmc';
+
%Initialize outputs
ix2 = [];
ilogpo2 = [];
@@ -79,22 +81,22 @@ d = chol(vv);
if ~options_.load_mh_file && ~options_.mh_recover
% Here we start a new Metropolis-Hastings, previous draws are discarded.
if NumberOfBlocks > 1
- disp('Estimation::mcmc: Multiple chains mode.')
+ fprintf('%s: Multiple chains mode.\n',dispString);
else
- disp('Estimation::mcmc: One Chain mode.')
+ fprintf('%s: One Chain mode.\n',dispString);
end
% Delete old mh files if any...
files = dir([BaseName '_mh*_blck*.mat']);
if length(files)
delete([BaseName '_mh*_blck*.mat']);
- disp('Estimation::mcmc: Old mh-files successfully erased!')
+ fprintf('%s: Old mh-files successfully erased!\n',dispString);
end
% Delete old Metropolis log file.
file = dir([ MetropolisFolder '/metropolis.log']);
if length(file)
delete([ MetropolisFolder '/metropolis.log']);
- disp('Estimation::mcmc: Old metropolis.log file successfully erased!')
- disp('Estimation::mcmc: Creation of a new metropolis.log file.')
+ fprintf('%s: Old metropolis.log file successfully erased!\n',dispString)
+ fprintf('%s: Creation of a new metropolis.log file.\n',dispString)
end
fidlog = fopen([MetropolisFolder '/metropolis.log'],'w');
fprintf(fidlog,'%% MH log file (Dynare).\n');
@@ -116,8 +118,8 @@ if ~options_.load_mh_file && ~options_.mh_recover
%% check for proper filesep char in user defined paths
RecordFile0=strrep(RecordFile0,'\',filesep);
if isempty(dir(RecordFile0))
- disp('Estimation::mcmc: wrong value for mh_initialize_from_previous_mcmc_record option')
- error('Estimation::mcmc: path to record file is not found')
+ fprintf('%s: Wrong value for mh_initialize_from_previous_mcmc_record option.\n',dispString);
+ error('%s: Path to record file is not found!',dispString)
else
record0=load(RecordFile0);
end
@@ -133,31 +135,31 @@ if ~options_.load_mh_file && ~options_.mh_recover
record0=load_last_mh_history_file(MetropolisFolder0, ModelName0);
end
if ~isnan(record0.MCMCConcludedSuccessfully) && ~record0.MCMCConcludedSuccessfully
- error('Estimation::mcmc: You are trying to load an MCMC that did not finish successfully. Please use mh_recover.')
+ error('%s: You are trying to load an MCMC that did not finish successfully. Please use ''mh_recover''!',dispString);
end
% mh_files = dir([ MetropolisFolder0 filesep ModelName0 '_mh*.mat']);
% if ~length(mh_files)
-% error('Estimation::mcmc: I cannot find any MH file to load here!')
+% error('%s: I cannot find any MH file to load here!',dispString)
% end
- disp('Estimation::mcmc: Initializing from past Metropolis-Hastings simulations...')
- disp(['Estimation::mcmc: Past MH path ' MetropolisFolder0 ])
- disp(['Estimation::mcmc: Past model name ' ModelName0 ])
+ fprintf('%s: Initializing from past Metropolis-Hastings simulations...\n',dispString);
+ fprintf('%s: Past MH path %s\n',dispString,MetropolisFolder0);
+ fprintf('%s: Past model name %s\n', dispString, ModelName0);
fprintf(fidlog,' Loading initial values from previous MH\n');
fprintf(fidlog,' Past MH path: %s\n', MetropolisFolder0 );
fprintf(fidlog,' Past model name: %s\n', ModelName0);
fprintf(fidlog,' \n');
past_number_of_blocks = record0.Nblck;
if past_number_of_blocks ~= NumberOfBlocks
- disp('Estimation::mcmc: The specified number of blocks doesn''t match with the previous number of blocks!')
- disp(['Estimation::mcmc: You declared ' int2str(NumberOfBlocks) ' blocks, but the previous number of blocks was ' int2str(past_number_of_blocks) '.'])
- disp(['Estimation::mcmc: I will run the Metropolis-Hastings with ' int2str(past_number_of_blocks) ' blocks.' ])
+ fprintf('%s: The specified number of blocks doesn''t match with the previous number of blocks!\n', dispString);
+ fprintf('%s: You declared %u blocks, but the previous number of blocks was %u.\n', dispString, NumberOfBlocks, past_number_of_blocks);
+ fprintf('%s: I will run the Metropolis-Hastings with %u block.\n', dispString, past_number_of_blocks);
NumberOfBlocks = past_number_of_blocks;
options_.mh_nblck = NumberOfBlocks;
end
if ~isempty(PriorFile0)
if isempty(dir(PriorFile0))
- disp('Estimation::mcmc: wrong value for mh_initialize_from_previous_mcmc_prior option')
- error('Estimation::mcmc: path to prior file is not found')
+ fprintf('%s: Wrong value for mh_initialize_from_previous_mcmc_prior option.', dispString);
+ error('%s: Path to prior file is not found!',dispString);
else
bayestopt0 = load(PriorFile0);
end
@@ -177,7 +179,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
if NumberOfBlocks > 1 || options_.mh_initialize_from_previous_mcmc.status% Case 1: multiple chains
set_dynare_seed('default');
fprintf(fidlog,[' Initial values of the parameters:\n']);
- disp('Estimation::mcmc: Searching for initial values...')
+ fprintf('%s: Searching for initial values...\n', dispString);
if ~options_.mh_initialize_from_previous_mcmc.status
ix2 = zeros(NumberOfBlocks,npar);
ilogpo2 = zeros(NumberOfBlocks,1);
@@ -217,56 +219,54 @@ if ~options_.load_mh_file && ~options_.mh_recover
end
init_iter = init_iter + 1;
if init_iter > 100 && validate == 0
- disp(['Estimation::mcmc: I couldn''t get a valid initial value in 100 trials.'])
+ fprintf('%s: I couldn''t get a valid initial value in 100 trials.\n', dispString);
if options_.nointeractive
- disp(['Estimation::mcmc: I reduce mh_init_scale_factor by 10 percent:'])
+ fprintf('%s: I reduce ''mh_init_scale_factor'' by 10 percent:\n', dispString);
if isfield(options_,'mh_init_scale')
options_.mh_init_scale = .9*options_.mh_init_scale;
- fprintf('Estimation::mcmc: Parameter mh_init_scale is now equal to %f.\n',options_.mh_init_scale)
+ fprintf('%s: Parameter ''mh_init_scale'' is now equal to %f.\n',dispString,options_.mh_init_scale);
else
options_.mh_init_scale_factor = .9*options_.mh_init_scale_factor;
- fprintf('Estimation::mcmc: Parameter mh_init_scale_factor is now equal to %f.\n',options_.mh_init_scale_factor)
+ fprintf('%s: Parameter ''mh_init_scale_factor'' is now equal to %f.\n', dispString,options_.mh_init_scale_factor);
end
- fprintf('Estimation::mcmc: Parameter mh_init_scale_factor is now equal to %f.\n',options_.mh_init_scale_factor)
+ fprintf('%s: Parameter ''mh_init_scale_factor'' is now equal to %f.\n', dispString,options_.mh_init_scale_factor);
else
if isfield(options_,'mh_init_scale')
- disp(['Estimation::mcmc: You should reduce mh_init_scale...'])
- fprintf('Estimation::mcmc: Parameter mh_init_scale is equal to %f.\n',options_.mh_init_scale)
- options_.mh_init_scale_factor = input('Estimation::mcmc: Enter a new value... ');
+ fprintf('%s: You should reduce mh_init_scale...\n',dispString);
+ fprintf('%s: Parameter ''mh_init_scale'' is equal to %f.\n',dispString,options_.mh_init_scale);
+ options_.mh_init_scale_factor = input(sprintf('%s: Enter a new value... ',dispString));
else
- disp(['Estimation::mcmc: You should reduce mh_init_scale_factor...'])
- fprintf('Estimation::mcmc: Parameter mh_init_scale_factor is equal to %f.\n',options_.mh_init_scale_factor)
- options_.mh_init_scale_factor = input('Estimation::mcmc: Enter a new value... ');
+ fprintf('%s: You should reduce ''mh_init_scale_factor''...\n',dispString);
+ fprintf('%s: Parameter ''mh_init_scale_factor'' is equal to %f.\n',dispString,options_.mh_init_scale_factor);
+ options_.mh_init_scale_factor = input(sprintf('%s: Enter a new value... ',dispString));
end
end
trial = trial+1;
end
end
if trial > 10 && ~validate
- disp(['Estimation::mcmc: I''m unable to find a starting value for block ' int2str(j)])
+ fprintf('%s: I''m unable to find a starting value for block %u.', dispString,j);
fclose(fidlog);
return
end
end
fprintf(fidlog,' \n');
- disp('Estimation::mcmc: Initial values found!')
- skipline()
+ fprintf('%s: Initial values found!\n\n',dispString);
else% Case 2: one chain (we start from the posterior mode)
fprintf(fidlog,[' Initial values of the parameters:\n']);
candidate = transpose(xparam1(:));%
if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
ix2 = candidate;
ilogpo2 = - feval(TargetFun,ix2',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
- disp('Estimation::mcmc: Initialization at the posterior mode.')
- skipline()
+ fprintf('%s: Initialization at the posterior mode.\n\n',dispString);
fprintf(fidlog,[' Blck ' int2str(1) 'params:\n']);
for i=1:length(ix2(1,:))
fprintf(fidlog,[' ' int2str(i) ':' num2str(ix2(1,i)) '\n']);
end
fprintf(fidlog,[' Blck ' int2str(1) 'logpo2:' num2str(ilogpo2) '\n']);
else
- disp('Estimation::mcmc: Initialization failed...')
- disp('Estimation::mcmc: The posterior mode lies outside the prior bounds.')
+ fprintf('%s: Initialization failed...\n',dispString);
+ fprintf('%s: The posterior mode lies outside the prior bounds.\n',dispString);
fclose(fidlog);
return
end
@@ -279,7 +279,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
% Delete the mh-history files
delete_mh_history_files(MetropolisFolder, ModelName);
% Create a new record structure
- fprintf(['Estimation::mcmc: Write details about the MCMC... ']);
+ fprintf('%s: Write details about the MCMC... ', dispString);
AnticipatedNumberOfFiles = ceil(nruns(1)/MAX_nruns);
AnticipatedNumberOfLinesInTheLastFile = nruns(1) - (AnticipatedNumberOfFiles-1)*MAX_nruns;
record.Sampler = options_.posterior_sampler_options.posterior_sampling_method;
@@ -312,8 +312,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
record.ProposalCovariance=d;
fprintf('Ok!\n');
id = write_mh_history_file(MetropolisFolder, ModelName, record);
- disp(['Estimation::mcmc: Details about the MCMC are available in ' BaseName '_mh_history_' num2str(id) '.mat'])
- skipline()
+ fprintf('%s: Details about the MCMC are available in %s_mh_history_%u.mat\n\n', dispString,BaseName,id);
fprintf(fidlog,[' CREATION OF THE MH HISTORY FILE!\n\n']);
fprintf(fidlog,[' Expected number of files per block.......: ' int2str(AnticipatedNumberOfFiles) '.\n']);
fprintf(fidlog,[' Expected number of lines in the last file: ' int2str(AnticipatedNumberOfLinesInTheLastFile) '.\n']);
@@ -332,15 +331,15 @@ if ~options_.load_mh_file && ~options_.mh_recover
fclose(fidlog);
elseif options_.load_mh_file && ~options_.mh_recover
% Here we consider previous mh files (previous mh did not crash).
- disp('Estimation::mcmc: I am loading past Metropolis-Hastings simulations...')
+ fprintf('%s: I am loading past Metropolis-Hastings simulations...\n',dispString);
record=load_last_mh_history_file(MetropolisFolder, ModelName);
if ~isnan(record.MCMCConcludedSuccessfully) && ~record.MCMCConcludedSuccessfully
- error('Estimation::mcmc: You are trying to load an MCMC that did not finish successfully. Please use mh_recover.')
+ error('%s: You are trying to load an MCMC that did not finish successfully. Please use mh_recover.',dispString);
end
record.MCMCConcludedSuccessfully=0; %reset indicator for this run
mh_files = dir([ MetropolisFolder filesep ModelName '_mh*.mat']);
if ~length(mh_files)
- error('Estimation::mcmc: I cannot find any MH file to load here!')
+ error('%s: I cannot find any MH file to load here!',dispString);
end
fidlog = fopen([MetropolisFolder '/metropolis.log'],'a');
fprintf(fidlog,'\n');
@@ -351,9 +350,9 @@ elseif options_.load_mh_file && ~options_.mh_recover
fprintf(fidlog,' \n');
past_number_of_blocks = record.Nblck;
if past_number_of_blocks ~= NumberOfBlocks
- disp('Estimation::mcmc: The specified number of blocks doesn''t match with the previous number of blocks!')
- disp(['Estimation::mcmc: You declared ' int2str(NumberOfBlocks) ' blocks, but the previous number of blocks was ' int2str(past_number_of_blocks) '.'])
- disp(['Estimation::mcmc: I will run the Metropolis-Hastings with ' int2str(past_number_of_blocks) ' blocks.' ])
+ fprintf('%s: The specified number of blocks doesn''t match with the previous number of blocks!\n',dispString);
+ fprintf('%s: You declared %u blocks, but the previous number of blocks was %u.\n', dispString,NumberOfBlocks,past_number_of_blocks);
+ fprintf('%s: I will run the Metropolis-Hastings with %u blocks.\n', dispString,past_number_of_blocks);
NumberOfBlocks = past_number_of_blocks;
options_.mh_nblck = NumberOfBlocks;
end
@@ -369,10 +368,10 @@ elseif options_.load_mh_file && ~options_.mh_recover
end
ilogpo2 = record.LastLogPost;
ix2 = record.LastParameters;
- [d,bayestopt_,record]=set_proposal_density_to_previous_value(record,options_,bayestopt_,d);
+ [d,bayestopt_,record]=set_proposal_density_to_previous_value(record,options_,bayestopt_,d,dispString);
FirstBlock = 1;
NumberOfPreviousSimulations = sum(record.MhDraws(:,1),1);
- fprintf('Estimation::mcmc: I am writing a new mh-history file... ');
+ fprintf('%s: I am writing a new mh-history file... ',dispString);
record.MhDraws = [record.MhDraws;zeros(1,3)];
NumberOfDrawsWrittenInThePastLastFile = MAX_nruns - LastLineNumber;
NumberOfDrawsToBeSaved = nruns(1) - NumberOfDrawsWrittenInThePastLastFile;
@@ -387,28 +386,27 @@ elseif options_.load_mh_file && ~options_.mh_recover
record.InitialSeeds = record.LastSeeds;
write_mh_history_file(MetropolisFolder, ModelName, record);
fprintf('Done.\n')
- disp(['Estimation::mcmc: Ok. I have loaded ' int2str(NumberOfPreviousSimulations) ' simulations.'])
- skipline()
+ fprintf('%s: Ok. I have loaded %u simulations.\n\n', dispString,NumberOfPreviousSimulations);
fclose(fidlog);
elseif options_.mh_recover
% The previous metropolis-hastings crashed before the end! I try to recover the saved draws...
- disp('Estimation::mcmc: Recover mode!')
+ fprintf('%s: Recover mode!\n',dispString);
record=load_last_mh_history_file(MetropolisFolder, ModelName);
NumberOfBlocks = record.Nblck;% Number of "parallel" mcmc chains.
options_.mh_nblck = NumberOfBlocks;
%% check consistency of options
if record.MhDraws(end,1)~=options_.mh_replic
- fprintf('\nEstimation::mcmc: You cannot specify a different mh_replic than in the chain you are trying to recover\n')
- fprintf('Estimation::mcmc: I am resetting mh_replic to %u\n',record.MhDraws(end,1))
- options_.mh_replic=record.MhDraws(end,1);
+ fprintf('\n%s: You cannot specify a different mh_replic than in the chain you are trying to recover\n',dispString);
+ fprintf('%s: I am resetting mh_replic to %u\n',dispString,record.MhDraws(end,1));
+ options_.mh_replic = record.MhDraws(end,1);
nruns = ones(NumberOfBlocks,1)*options_.mh_replic;
end
if ~isnan(record.MAX_nruns(end,1)) %field exists
if record.MAX_nruns(end,1)~=MAX_nruns
- fprintf('\nEstimation::mcmc: You cannot specify a different MaxNumberOfBytes than in the chain you are trying to recover\n')
- fprintf('Estimation::mcmc: I am resetting MAX_nruns to %u\n',record.MAX_nruns(end,1))
+ fprintf('\n%s: You cannot specify a different MaxNumberOfBytes than in the chain you are trying to recover\n',dispString);
+ fprintf('%s: I am resetting MAX_nruns to %u\n',dispString,record.MAX_nruns(end,1));
MAX_nruns=record.MAX_nruns(end,1);
end
end
@@ -451,7 +449,7 @@ elseif options_.mh_recover
LastFileFullIndicator=1;
end
if ~isequal(options_.posterior_sampler_options.posterior_sampling_method,'slice')
- [d,bayestopt_,record]=set_proposal_density_to_previous_value(record,options_,bayestopt_,d);
+ [d,bayestopt_,record]=set_proposal_density_to_previous_value(record,options_,bayestopt_,d,dispString);
end
%% Now find out what exactly needs to be redone
% 1. Check if really something needs to be done
@@ -464,10 +462,10 @@ elseif options_.mh_recover
% Quit if no crashed mcmc chain can be found as there are as many files as expected
if (TotalNumberOfMhFiles==ExpectedNumberOfMhFiles)
if isnumeric(options_.parallel)
- disp('Estimation::mcmc: It appears that you don''t need to use the mh_recover option!')
- disp(' You have to edit the mod file and remove the mh_recover option')
- disp(' in the estimation command')
- error('Estimation::mcmc: mh_recover option not required!')
+ fprintf('%s: It appears that you don''t need to use the mh_recover option!\n',dispString);
+ fprintf(' You have to edit the mod file and remove the mh_recover option\n');
+ fprintf(' in the estimation command.\n');
+ error('%s: mh_recover option not required!',dispString)
end
end
% 2. Something needs to be done; find out what
@@ -482,10 +480,10 @@ elseif options_.mh_recover
FBlock = zeros(NumberOfBlocks,1);
while FirstBlock <= NumberOfBlocks
if NumberOfMhFilesPerBlock(FirstBlock) < ExpectedNumberOfMhFilesPerBlock
- disp(['Estimation::mcmc: Chain ' int2str(FirstBlock) ' is not complete!'])
+ fprintf('%s: Chain %u is not complete!\n', dispString,FirstBlock);
FBlock(FirstBlock)=1;
else
- disp(['Estimation::mcmc: Chain ' int2str(FirstBlock) ' is complete!'])
+ fprintf('%s: Chain %u is complete!\n', dispString,FirstBlock);
end
FirstBlock = FirstBlock+1;
end
@@ -537,8 +535,8 @@ elseif options_.mh_recover
record.InitialSeeds(FirstBlock).Unifor=loaded_results.LastSeeds.(['file' int2str(NumberOfSavedMhFilesInTheCrashedBlck)]).Unifor;
record.InitialSeeds(FirstBlock).Normal=loaded_results.LastSeeds.(['file' int2str(NumberOfSavedMhFilesInTheCrashedBlck)]).Normal;
else
- fprintf('Estimation::mcmc: You are trying to recover a chain generated with an older Dynare version.\n')
- fprintf('Estimation::mcmc: I am using the default seeds to continue the chain.\n')
+ fprintf('%s: You are trying to recover a chain generated with an older Dynare version.\n',dispString);
+ fprintf('%s: I am using the default seeds to continue the chain.\n',dispString);
end
if update_record
update_last_mh_history_file(MetropolisFolder, ModelName, record);
@@ -557,8 +555,8 @@ elseif options_.mh_recover
record.LastSeeds(FirstBlock).Unifor=loaded_results.LastSeeds.(['file' int2str(ExpectedNumberOfMhFilesPerBlock)]).Unifor;
record.LastSeeds(FirstBlock).Normal=loaded_results.LastSeeds.(['file' int2str(ExpectedNumberOfMhFilesPerBlock)]).Normal;
else
- fprintf('Estimation::mcmc: You are trying to recover a chain generated with an older Dynare version.\n')
- fprintf('Estimation::mcmc: I am using the default seeds to continue the chain.\n')
+ fprintf('%s: You are trying to recover a chain generated with an older Dynare version.\n',dispString);
+ fprintf('%s: I am using the default seeds to continue the chain.\n',dispString);
end
if isfield(loaded_results,'accepted_draws_this_chain')
record.AcceptanceRatio(FirstBlock)=loaded_results.accepted_draws_this_chain/record.MhDraws(end,1);
@@ -572,32 +570,32 @@ elseif options_.mh_recover
FirstBlock = find(FBlock==1,1);
end
-function [d,bayestopt_,record]=set_proposal_density_to_previous_value(record,options_,bayestopt_,d)
+function [d,bayestopt_,record]=set_proposal_density_to_previous_value(record,options_,bayestopt_,d,dispString)
if ~options_.use_mh_covariance_matrix
if isfield(record,'ProposalCovariance') && isfield(record,'ProposalScaleVec')
- fprintf('Estimation::mcmc: Recovering the previous proposal density.\n')
+ fprintf('%s: Recovering the previous proposal density.\n',dispString);
d=record.ProposalCovariance;
bayestopt_.jscale=record.ProposalScaleVec;
else
if ~isequal(options_.posterior_sampler_options.posterior_sampling_method,'slice')
% pass through input d unaltered
if options_.mode_compute~=0
- fprintf('Estimation::mcmc: No stored previous proposal density found, continuing with the one implied by mode_compute\n.');
+ fprintf('%s: No stored previous proposal density found, continuing with the one implied by mode_compute.\n',dispString);
elseif ~isempty(options_.mode_file)
- fprintf('Estimation::mcmc: No stored previous proposal density found, continuing with the one implied by the mode_file\n.');
+ fprintf('%s: No stored previous proposal density found, continuing with the one implied by the mode_file.\n',dispString);
else
- error('Estimation::mcmc: No stored previous proposal density found, no mode-finding conducted, and no mode-file provided. I don''t know how to continue')
+ error('%s: No stored previous proposal density found, no mode-finding conducted, and no mode-file provided. I don''t know how to continue!',dispString);
end
end
end
else
% pass through input d unaltered
- fprintf('Estimation::mcmc: use_mh_covariance_matrix specified, continuing with proposal density implied by the previous MCMC run.\n.');
+ fprintf('%s: ''use_mh_covariance_matrix'' specified, continuing with proposal density implied by the previous MCMC run.\n',dispString);
end
if isfield(record,'Sampler')
if ~strcmp(record.Sampler,options_.posterior_sampler_options.posterior_sampling_method)
- warning('Estimation::mcmc: The posterior_sampling_method %s selected differs from the %s of the original chain. This may create problems with the convergence diagnostics.',options_.posterior_sampler_options.posterior_sampling_method,record.Sampler)
+ warning('%s: The posterior_sampling_method %s selected differs from the %s of the original chain. This may create problems with the convergence diagnostics.',dispString,options_.posterior_sampler_options.posterior_sampling_method,record.Sampler)
record.Sampler=options_.posterior_sampler_options.posterior_sampling_method; %update sampler used
end
end
diff --git a/matlab/set_prior.m b/matlab/set_prior.m
index bc4fff302d56c8aa5209896ef9c3979f727607e7..635568dc861804863848ddc499ba25568e2b880e 100644
--- a/matlab/set_prior.m
+++ b/matlab/set_prior.m
@@ -5,7 +5,7 @@ function [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params
% INPUTS
% o estim_params_ [structure] characterizing parameters to be estimated.
% o M_ [structure] characterizing the model.
-% o options_ [structure]
+% o options_ [structure] characterizing the options.
%
% OUTPUTS
% o xparam1 [double] vector of parameters to be estimated (initial values)
@@ -18,7 +18,7 @@ function [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params
% SPECIAL REQUIREMENTS
% None
-% Copyright © 2003-2018 Dynare Team
+% Copyright © 2003-2023 Dynare Team
%
% This file is part of Dynare.
%
diff --git a/matlab/utilities/estimation/check_hessian_at_the_mode.m b/matlab/utilities/estimation/check_hessian_at_the_mode.m
new file mode 100644
index 0000000000000000000000000000000000000000..8fa880253fcc8555590b572b75cc5c8a62640eb6
--- /dev/null
+++ b/matlab/utilities/estimation/check_hessian_at_the_mode.m
@@ -0,0 +1,69 @@
+function check_hessian_at_the_mode(hessian_xparam1, xparam1, M_, estim_params_, options_, bounds)
+% check_hessian_at_the_mode(hessian_xparam1, xparam1, M_, estim_params_, options_, bounds)
+% -------------------------------------------------------------------------
+% This function checks whether the hessian matrix at the mode is positive definite.
+% =========================================================================
+% INPUTS
+% o hessian_xparam1: [matrix] hessian matrix at the mode
+% o xparam1: [vector] vector of parameter values at the mode
+% o M_: [structure] information about model
+% o estim_params_: [structure] information about estimated parameters
+% o options_: [structure] information about options
+% o bounds: [structure] information about bounds
+% -------------------------------------------------------------------------
+% OUTPUTS
+% none, displays a warning message if the hessian matrix is not positive definite
+% -------------------------------------------------------------------------
+% This function is called by
+% o dynare_estimation_1.m
+% -------------------------------------------------------------------------
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+try
+ chol(hessian_xparam1);
+catch
+ tol_bounds = 1.e-10;
+ skipline()
+ disp('OPTIMIZATION PROBLEM!')
+ disp(' (minus) the hessian matrix at the "mode" is not positive definite!')
+ disp('=> variance of the estimated parameters are not positive.')
+ disp('You should try to change the initial values of the parameters using')
+ disp('the estimated_params_init block, or use another optimization routine.')
+ params_at_bound = find(abs(xparam1-bounds.ub)<tol_bounds | abs(xparam1-bounds.lb)<tol_bounds);
+ if ~isempty(params_at_bound)
+ for ii=1:length(params_at_bound)
+ params_at_bound_name{ii,1}=get_the_name(params_at_bound(ii),0,M_,estim_params_,options_);
+ end
+ disp_string=[params_at_bound_name{1,:}];
+ for ii=2:size(params_at_bound_name,1)
+ disp_string=[disp_string,', ',params_at_bound_name{ii,:}];
+ end
+ fprintf('\nThe following parameters are at the bound: %s\n', disp_string)
+ fprintf('Some potential solutions are:\n')
+ fprintf(' - Check your model for mistakes.\n')
+ fprintf(' - Check whether model and data are consistent (correct observation equation).\n')
+ fprintf(' - Shut off prior_trunc.\n')
+ fprintf(' - Change the optimization bounds.\n')
+ fprintf(' - Use a different mode_compute like 6 or 9.\n')
+ fprintf(' - Check whether the parameters estimated are identified.\n')
+ fprintf(' - Check prior shape (e.g. Inf density at bound(s)).\n')
+ fprintf(' - Increase the informativeness of the prior.\n')
+ end
+ warning('The results below are most likely wrong!');
+end
\ No newline at end of file
diff --git a/matlab/utilities/estimation/check_mode_file.m b/matlab/utilities/estimation/check_mode_file.m
new file mode 100644
index 0000000000000000000000000000000000000000..29ddb1e84690817d6d94d34daf3711ece75449f5
--- /dev/null
+++ b/matlab/utilities/estimation/check_mode_file.m
@@ -0,0 +1,163 @@
+function [xparam1, hh] = check_mode_file(xparam1, hh, options_, bayestopt_)
+% function [xparam1, hh] = check_mode_file(xparam1, hh, options_, bayestopt_)
+% -------------------------------------------------------------------------
+% Check that the provided mode_file is compatible with the current estimation settings.
+% =========================================================================
+% INPUTS
+% o xparam1: [vector] current vector of parameter values at the mode
+% o hh: [matrix] current hessian matrix at the mode
+% o options_: [structure] information about options
+% o bayestopt_: [structure] information about priors
+% -------------------------------------------------------------------------
+% OUTPUTS
+% o xparam1: [vector] updated vector of parameter values at the mode
+% o hh: [matrix] updated hessian matrix at the mode
+% -------------------------------------------------------------------------
+% This function is called by
+% o dynare_estimation_init.m
+% -------------------------------------------------------------------------
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+number_of_estimated_parameters = length(xparam1);
+mode_file = load(options_.mode_file);
+if number_of_estimated_parameters>length(mode_file.xparam1)
+ % More estimated parameters than parameters in the mode_file.
+ skipline()
+ disp(['The ''mode_file'' ' options_.mode_file ' has been generated using another specification of the model or another model!'])
+ disp(['Your file contains estimates for ' int2str(length(mode_file.xparam1)) ' parameters, while you are attempting to estimate ' int2str(number_of_estimated_parameters) ' parameters:'])
+ md = []; xd = [];
+ for i=1:number_of_estimated_parameters
+ id = strmatch(deblank(bayestopt_.name(i,:)),mode_file.parameter_names,'exact');
+ if isempty(id)
+ disp(['--> Estimated parameter ' bayestopt_.name{i} ' is not present in the loaded ''mode_file'' (prior mean or initialized values will be used, if possible).'])
+ else
+ xd = [xd; i];
+ md = [md; id];
+ end
+ end
+ for i=1:length(mode_file.xparam1)
+ id = strmatch(mode_file.parameter_names{i},bayestopt_.name,'exact');
+ if isempty(id)
+ disp(['--> Parameter ' mode_file.parameter_names{i} ' is not estimated according to the current mod file.'])
+ end
+ end
+ if ~options_.mode_compute
+ % The mode is not estimated.
+ error('Please change the ''mode_file'' option, the list of estimated parameters or set ''mode_compute''>0.')
+ else
+ % The mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean or initialized values.
+ if ~isempty(xd)
+ xparam1(xd) = mode_file.xparam1(md);
+ else
+ error('Please remove the ''mode_file'' option.')
+ end
+ end
+elseif number_of_estimated_parameters<length(mode_file.xparam1)
+ % Less estimated parameters than parameters in the mode_file.
+ skipline()
+ disp(['The ''mode_file'' ' options_.mode_file ' has been generated using another specification of the model or another model!'])
+ disp(['Your file contains estimates for ' int2str(length(mode_file.xparam1)) ' parameters, while you are attempting to estimate only ' int2str(number_of_estimated_parameters) ' parameters:'])
+ md = []; xd = [];
+ for i=1:number_of_estimated_parameters
+ id = strmatch(deblank(bayestopt_.name(i,:)),mode_file.parameter_names,'exact');
+ if isempty(id)
+ disp(['--> Estimated parameter ' deblank(bayestopt_.name(i,:)) ' is not present in the loaded ''mode_file'' (prior mean or initialized values will be used, if possible).'])
+ else
+ xd = [xd; i];
+ md = [md; id];
+ end
+ end
+ for i=1:length(mode_file.xparam1)
+ id = strmatch(mode_file.parameter_names{i},bayestopt_.name,'exact');
+ if isempty(id)
+ disp(['--> Parameter ' mode_file.parameter_names{i} ' is not estimated according to the current mod file.'])
+ end
+ end
+ if ~options_.mode_compute
+ % The posterior mode is not estimated. If possible, fix the mode_file.
+ if isequal(length(xd),number_of_estimated_parameters)
+ disp('==> Fix mode_file (remove unused parameters).')
+ xparam1 = mode_file.xparam1(md);
+ if isfield(mode_file,'hh')
+ hh = mode_file.hh(md,md);
+ end
+ else
+ error('Please change the ''mode_file'' option, the list of estimated parameters or set ''mode_compute''>0.')
+ end
+ else
+ % The mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode_file using the prior mean or initialized values.
+ if ~isempty(xd)
+ xparam1(xd) = mode_file.xparam1(md);
+ else
+ % None of the estimated parameters are present in the mode_file.
+ error('Please remove the ''mode_file'' option.')
+ end
+ end
+else
+ % The number of declared estimated parameters match the number of parameters in the mode file.
+ % Check that the parameters in the mode file and according to the current mod file are identical.
+ if ~isfield(mode_file,'parameter_names')
+ disp(['The ''mode_file'' ' options_.mode_file ' has been generated using an older version of Dynare. It cannot be verified if it matches the present model. Proceed at your own risk.'])
+ mode_file.parameter_names=deblank(bayestopt_.name); %set names
+ end
+ if isequal(mode_file.parameter_names, bayestopt_.name)
+ xparam1 = mode_file.xparam1;
+ if isfield(mode_file,'hh')
+ hh = mode_file.hh;
+ end
+ else
+ skipline()
+ disp(['The ''mode_file'' ' options_.mode_file ' has been generated using another specification of the model or another model!'])
+ % Check if this is only an ordering issue or if the missing parameters can be initialized with the prior mean.
+ md = []; xd = [];
+ for i=1:number_of_estimated_parameters
+ id = strmatch(deblank(bayestopt_.name(i,:)), mode_file.parameter_names,'exact');
+ if isempty(id)
+ disp(['--> Estimated parameter ' bayestopt_.name{i} ' is not present in the loaded ''mode_file''.'])
+ else
+ xd = [xd; i];
+ md = [md; id];
+ end
+ end
+ if ~options_.mode_compute
+ % The mode is not estimated
+ if isequal(length(xd), number_of_estimated_parameters)
+ % This is an ordering issue.
+ xparam1 = mode_file.xparam1(md);
+ if isfield(mode_file,'hh')
+ hh = mode_file.hh(md,md);
+ end
+ else
+ error('Please change the ''mode_file'' option, the list of estimated parameters or set ''mode_compute''>0.')
+ end
+ else
+ % The mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode_file using the prior mean or initialized values.
+ if ~isempty(xd)
+ xparam1(xd) = mode_file.xparam1(md);
+ if isfield(mode_file,'hh')
+ hh(xd,xd) = mode_file.hh(md,md);
+ end
+ else
+ % None of the estimated parameters are present in the mode_file.
+ error('Please remove the ''mode_file'' option.')
+ end
+ end
+ end
+end
+skipline()
\ No newline at end of file
diff --git a/matlab/utilities/estimation/check_prior_stderr_corr.m b/matlab/utilities/estimation/check_prior_stderr_corr.m
new file mode 100644
index 0000000000000000000000000000000000000000..0cdc7b71c1b6c6a8d9b74c1a1349331102f5304c
--- /dev/null
+++ b/matlab/utilities/estimation/check_prior_stderr_corr.m
@@ -0,0 +1,53 @@
+function check_prior_stderr_corr(estim_params_,bayestopt_)
+% function check_prior_stderr_corr(estim_params_,bayestopt_)
+% -------------------------------------------------------------------------
+% Check if the prior allows for negative standard deviations and
+% correlations larger than +-1. If so, issue a warning.
+% =========================================================================
+% INPUTS
+% o estim_params_: [struct] information on estimated parameters
+% o bayestopt_: [struct] information on priors
+% -------------------------------------------------------------------------
+% OUTPUTS
+% none, but issues a warning if the prior allows for negative standard
+% -------------------------------------------------------------------------
+% This function is called by
+% o initial_estimation_checks.m
+% -------------------------------------------------------------------------
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+nvx = estim_params_.nvx; % number of stderr parameters for structural shocks
+if nvx && any(bayestopt_.p3(1:nvx)<0)
+ warning('Your prior allows for negative standard deviations for structural shocks. Due to working with variances, Dynare will be able to continue, but it is recommended to change your prior.')
+end
+offset = nvx;
+nvn = estim_params_.nvn; % number of stderr parameters for measurement errors
+if nvn && any(bayestopt_.p3(1+offset:offset+nvn)<0)
+ warning('Your prior allows for negative standard deviations for measurement error. Due to working with variances, Dynare will be able to continue, but it is recommended to change your prior.')
+end
+offset = nvx+nvn;
+ncx = estim_params_.ncx; % number of corr parameters for structural shocks
+if ncx && (any(bayestopt_.p3(1+offset:offset+ncx)<-1) || any(bayestopt_.p4(1+offset:offset+ncx)>1))
+ warning('Your prior allows for correlations between structural shocks larger than +-1 and will not integrate to 1 due to truncation. Please change your prior')
+end
+offset = nvx+nvn+ncx;
+ncn = estim_params_.ncn; % number of corr parameters for measurement errors
+if ncn && (any(bayestopt_.p3(1+offset:offset+ncn)<-1) || any(bayestopt_.p4(1+offset:offset+ncn)>1))
+ warning('Your prior allows for correlations between measurement errors larger than +-1 and will not integrate to 1 due to truncation. Please change your prior')
+end
\ No newline at end of file
diff --git a/matlab/utilities/estimation/check_steady_state_changes_parameters.m b/matlab/utilities/estimation/check_steady_state_changes_parameters.m
new file mode 100644
index 0000000000000000000000000000000000000000..1cd40b29306dff4467daf6f09dc924c6c519a54f
--- /dev/null
+++ b/matlab/utilities/estimation/check_steady_state_changes_parameters.m
@@ -0,0 +1,61 @@
+function [steady_state, info, steady_state_changes_parameters] = check_steady_state_changes_parameters(M_,estim_params_,oo_,options_,steadystate_check_flag_vec)
+% function [steady_state, info, steady_state_changes_parameters] = check_steady_state_changes_parameters(M_,estim_params_,oo_,options_,steadystate_check_flag_vec)
+% -------------------------------------------------------------------------
+% Check if steady-state solves static model and if it changes estimated parameters
+% =========================================================================
+% INPUTS
+% o M_: [struct] information on the model
+% o estim_params_: [struct] information on estimated parameters
+% o oo_: [struct] results
+% o options_: [struct] information on options
+% o steadystate_check_flag_vec: [vector] steadystate_check_flag for both checks (might be different in case of diffuse_filter)
+% -------------------------------------------------------------------------
+% OUTPUTS
+% o steady_state: [vector] steady state
+% o info: [scalar] 0 if steady state solves static model
+% o steady_state_changes_parameters: [logical] true if steady state file changes estimated parameters
+% -------------------------------------------------------------------------
+% This function is called by
+% o initial_estimation_checks.m
+% -------------------------------------------------------------------------
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+value_parameter_change = 1.01; % value with which parameters are slightly changed.
+steady_state_changes_parameters = false; % initialize
+
+% check if steady state solves static model
+[steady_state, new_steady_params, info] = evaluate_steady_state(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],M_,options_,steadystate_check_flag_vec(1));
+
+% check whether steady state file changes estimated parameters
+if isfield(estim_params_,'param_vals') && ~isempty(estim_params_.param_vals)
+ old_steady_params = M_.params; % save initial parameters
+ M_par_varied = M_; % store Model structure
+ M_par_varied.params(estim_params_.param_vals(:,1)) = M_par_varied.params(estim_params_.param_vals(:,1))*value_parameter_change; % vary parameters
+ [~, new_steady_params_2] = evaluate_steady_state(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],M_par_varied,options_,steadystate_check_flag_vec(2));
+ changed_par_indices = find((old_steady_params(estim_params_.param_vals(:,1))-new_steady_params(estim_params_.param_vals(:,1))) ...
+ | (M_par_varied.params(estim_params_.param_vals(:,1))-new_steady_params_2(estim_params_.param_vals(:,1))));
+ if ~isempty(changed_par_indices)
+ fprintf('\nThe steady state file internally changed the values of the following estimated parameters:\n')
+ disp(char(M_.param_names(estim_params_.param_vals(changed_par_indices,1))))
+ fprintf('This will override the parameter values and may lead to wrong results.\n')
+ fprintf('Check whether this is really intended.\n')
+ warning('The steady state file internally changes the values of the estimated parameters.')
+ steady_state_changes_parameters = true;
+ end
+end
\ No newline at end of file
diff --git a/matlab/utilities/estimation/check_varobs_are_endo_and_declared_once.m b/matlab/utilities/estimation/check_varobs_are_endo_and_declared_once.m
new file mode 100644
index 0000000000000000000000000000000000000000..75daffc917c77c61fa165bbe683300576b829f9b
--- /dev/null
+++ b/matlab/utilities/estimation/check_varobs_are_endo_and_declared_once.m
@@ -0,0 +1,50 @@
+function check_varobs_are_endo_and_declared_once(varobs,endo_names)
+% function check_varobs_are_endo_and_declared_once(varobs,endo_names)
+% -------------------------------------------------------------------------
+% Check that each declared observed variable:
+% - is also an endogenous variable
+% - is declared only once
+% =========================================================================
+% INPUTS
+% o varobs: [cell] list of observed variables
+% o endo_names: [cell] list of endogenous variables
+% -------------------------------------------------------------------------
+% OUTPUTS
+% none, display an error message something is wrong with VAROBS
+% -------------------------------------------------------------------------
+% This function is called by
+% o dynare_estimation_init.m
+% -------------------------------------------------------------------------
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+number_of_observed_variables = length(varobs);
+for i = 1:number_of_observed_variables
+ if ~any(strcmp(varobs{i},endo_names))
+ error(['VAROBS: unknown variable (' varobs{i} ')!'])
+ end
+end
+
+% Check that a variable is not declared as observed more than once.
+if length(unique(varobs))<length(varobs)
+ for i = 1:number_of_observed_variables
+ if sum(strcmp(varobs{i},varobs)) > 1
+ error(['VAROBS: a variable cannot be declared as observed more than once (' varobs{i} ')!'])
+ end
+ end
+end
\ No newline at end of file
diff --git a/matlab/utilities/estimation/set_mcmc_jumping_covariance.m b/matlab/utilities/estimation/set_mcmc_jumping_covariance.m
new file mode 100644
index 0000000000000000000000000000000000000000..b0cb99d49852e79a0dfee3e685138d4722668150
--- /dev/null
+++ b/matlab/utilities/estimation/set_mcmc_jumping_covariance.m
@@ -0,0 +1,67 @@
+function invhess = set_mcmc_jumping_covariance(invhess, xparam_nbr, MCMC_jumping_covariance, bayestopt_, stringForErrors)
+% function invhess = set_mcmc_jumping_covariance(invhess, xparam_nbr, MCMC_jumping_covariance, bayestopt_, stringForErrors)
+% -------------------------------------------------------------------------
+% sets the jumping covariance matrix for the MCMC algorithm
+% =========================================================================
+% INPUTS
+% o invhess: [matrix] already computed inverse of the hessian matrix
+% o xparam_nbr: [integer] number of estimated parameters
+% o MCMC_jumping_covariance: [string] name of option or file setting the jumping covariance matrix
+% o bayestopt_: [struct] information on priors
+% o stringForErrors: [string] string to be used in error messages
+% -------------------------------------------------------------------------
+% OUTPUTS
+% o invhess: [matrix] jumping covariance matrix
+% -------------------------------------------------------------------------
+% This function is called by
+% o dynare_estimation_1.m
+% -------------------------------------------------------------------------
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+switch MCMC_jumping_covariance
+ case 'hessian' % do nothing and use hessian from previous mode optimization
+ case 'prior_variance' % use prior variance
+ if any(isinf(bayestopt_.p2))
+ error('%s: Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.',stringForErrors);
+ else
+ hess = diag(1./(bayestopt_.p2.^2));
+ end
+ hsd = sqrt(diag(hess));
+ invhess = inv(hess./(hsd*hsd'))./(hsd*hsd');
+ case 'identity_matrix' % use identity
+ invhess = eye(xparam_nbr);
+ otherwise % user specified matrix in file
+ try
+ load(MCMC_jumping_covariance,'jumping_covariance')
+ hess = jumping_covariance;
+ catch
+ error(['%s: No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat!'],stringForErrors);
+ end
+ [nrow, ncol] = size(hess);
+ if ~isequal(nrow,ncol) && ~isequal(nrow,xparam_nbr) % check if square and right size
+ error(['%s: ''jumping_covariance'' matrix (loaded from ',options_.MCMC_jumping_covariance,'.mat) must be square and have ',num2str(xparam_nbr),' rows and columns!'],stringForErrors);
+ end
+ try % check for positive definiteness
+ chol(hess);
+ hsd = sqrt(diag(hess));
+ invhess = inv(hess./(hsd*hsd'))./(hsd*hsd');
+ catch
+ error('%s: Specified ''MCMC_jumping_covariance'' is not positive definite!',stringForErrors);
+ end
+end
\ No newline at end of file
diff --git a/matlab/utilities/estimation/set_mcmc_prior_bounds.m b/matlab/utilities/estimation/set_mcmc_prior_bounds.m
new file mode 100644
index 0000000000000000000000000000000000000000..76bbf09ca47f0c213f0d0b7fcd7b2611a02a6741
--- /dev/null
+++ b/matlab/utilities/estimation/set_mcmc_prior_bounds.m
@@ -0,0 +1,51 @@
+function bounds = set_mcmc_prior_bounds(xparam, bayestopt_, options_, stringForErrors)
+% function bounds = set_mcmc_prior_bounds(xparam, bayestopt_, options_, stringForErrors)
+% -------------------------------------------------------------------------
+% Reset bounds as lower and upper bounds must only be operational during mode-finding
+% =========================================================================
+% INPUTS
+% o xparam: [vector] vector of parameters
+% o bayestopt_: [struct] information on priors
+% o options_: [struct] Dynare options
+% o stringForErrors: [string] string to be used in error messages
+% -------------------------------------------------------------------------
+% OUTPUTS
+% o bounds: [struct] structure with fields lb and ub
+% -------------------------------------------------------------------------
+% This function is called by
+% o dynare_estimation_1.m
+% -------------------------------------------------------------------------
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+bounds = prior_bounds(bayestopt_, options_.prior_trunc);
+outside_bound_pars = find(xparam < bounds.lb | xparam > bounds.ub);
+if ~isempty(outside_bound_pars)
+ for ii = 1:length(outside_bound_pars)
+ outside_bound_par_names{ii,1} = get_the_name(ii,0,M_,estim_params_,options_);
+ end
+ disp_string = [outside_bound_par_names{1,:}];
+ for ii = 2:size(outside_bound_par_names,1)
+ disp_string = [disp_string,', ',outside_bound_par_names{ii,:}];
+ end
+ if options_.prior_trunc > 0
+ error(['%s: Mode value(s) of ', disp_string ,' are outside parameter bounds. Potentially, you should set prior_trunc=0!'],stringForErrors);
+ else
+ error(['%s: Mode value(s) of ', disp_string ,' are outside parameter bounds!'],stringForErrors);
+ end
+end
\ No newline at end of file
diff --git a/matlab/utilities/estimation/tune_mcmc_mh_jscale_wrapper.m b/matlab/utilities/estimation/tune_mcmc_mh_jscale_wrapper.m
new file mode 100644
index 0000000000000000000000000000000000000000..d0be1e6e87ba0a1f49e9ab5c81eb7f2bf74c6408
--- /dev/null
+++ b/matlab/utilities/estimation/tune_mcmc_mh_jscale_wrapper.m
@@ -0,0 +1,48 @@
+function mh_jscale = tune_mcmc_mh_jscale_wrapper(invhess, options_, M_, objective_function, xparam1, bounds, varargin)
+% function mh_jscale = tune_mcmc_mh_jscale_wrapper(invhess, options_, M_, objective_function, xparam1, bounds, varargin)
+% -------------------------------------------------------------------------
+% Wrapper to call the algorithm to tune the jumping scale parameter for the
+% Metropolis-Hastings algorithm; currently only supports RW-MH algorithm.
+% =========================================================================
+% INPUTS
+% o invhess: [matrix] jumping covariance matrix
+% o options_: [struct] Dynare options
+% o M_: [struct] Dynare model structure
+% o objective_function: [function handle] objective function
+% o xparam1: [vector] vector of estimated parameters at the mode
+% o bounds: [struct] structure containing information on bounds
+% o varargin: [cell] additional arguments to be passed to the objective function
+% -------------------------------------------------------------------------
+% OUTPUTS
+% o mh_jscale: [double] tuned jumping scale parameter
+% -------------------------------------------------------------------------
+% This function is called by
+% o dynare_estimation_1.m
+% -------------------------------------------------------------------------
+% Copyright © 2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+
+% get invhess in case of use_mh_covariance_matrix
+posterior_sampler_options_temp = options_.posterior_sampler_options.current_options;
+posterior_sampler_options_temp.invhess = invhess;
+posterior_sampler_options_temp = check_posterior_sampler_options(posterior_sampler_options_temp, M_.fname, M_.dname, options_);
+opt = options_.mh_tune_jscale;
+opt.rwmh = options_.posterior_sampler_options.rwmh;
+mh_jscale = calibrate_mh_scale_parameter(objective_function, ...
+ posterior_sampler_options_temp.invhess, xparam1, [bounds.lb,bounds.ub], ...
+ opt, varargin{:});
\ No newline at end of file
diff --git a/tests/estimation/method_of_moments/AnScho/AnScho_MoM_common.inc b/tests/estimation/method_of_moments/AnScho/AnScho_MoM_common.inc
index 6ba8a8f8ab4f56e8cb36bd12beecae0c7cc159be..dc2eb49718ca293c63ad52ef93b2418193b5a5e6 100644
--- a/tests/estimation/method_of_moments/AnScho/AnScho_MoM_common.inc
+++ b/tests/estimation/method_of_moments/AnScho/AnScho_MoM_common.inc
@@ -193,9 +193,10 @@ INT(0)^2*INFL(1)^4; %redundant
@#endif
end;
+M_.matched_moments_orig = M_.matched_moments;
method_of_moments(
-% Necessery options
+% Necessary options
mom_method = @{MoM_Method} % method of moments method; possible values: GMM|SMM
, datafile = 'AnScho_MoM_data_2.mat' % name of filename with data
diff --git a/tests/estimation/method_of_moments/RBC/RBC_MoM_Andreasen.mod b/tests/estimation/method_of_moments/RBC/RBC_MoM_Andreasen.mod
index 41dcdd8c97c5e8e73acefdd9c4d49159e60b0116..e8af3f66ae384ed7a0eded4c179dba430d31ab29 100644
--- a/tests/estimation/method_of_moments/RBC/RBC_MoM_Andreasen.mod
+++ b/tests/estimation/method_of_moments/RBC/RBC_MoM_Andreasen.mod
@@ -152,7 +152,7 @@ end
method_of_moments(
- % Necessery options
+ % Necessary options
mom_method = GMM % method of moments method; possible values: GMM|SMM
, datafile = 'RBC_Andreasen_Data_2.mat' % name of filename with data
diff --git a/tests/estimation/method_of_moments/RBC/RBC_MoM_SMM_ME.mod b/tests/estimation/method_of_moments/RBC/RBC_MoM_SMM_ME.mod
index 60920a15c52f588c2434c9a8aa9bdd3cde02fbc8..d8e6077deb89cb63fda0a7f4cd65e29bc03b898a 100644
--- a/tests/estimation/method_of_moments/RBC/RBC_MoM_SMM_ME.mod
+++ b/tests/estimation/method_of_moments/RBC/RBC_MoM_SMM_ME.mod
@@ -139,7 +139,7 @@ end
@#for mommethod in ["SMM"]
method_of_moments(
- % Necessery options
+ % Necessary options
mom_method = @{mommethod} % method of moments method; possible values: GMM|SMM
, datafile = 'RBC_MoM_data_@{orderApp}.mat' % name of filename with data
diff --git a/tests/estimation/method_of_moments/RBC/RBC_MoM_prefilter.mod b/tests/estimation/method_of_moments/RBC/RBC_MoM_prefilter.mod
index 00b89a5a2a3086566f4d335489bd458f3f99deeb..6bc36f3d41ed6e2251dccfaba22792559701447c 100644
--- a/tests/estimation/method_of_moments/RBC/RBC_MoM_prefilter.mod
+++ b/tests/estimation/method_of_moments/RBC/RBC_MoM_prefilter.mod
@@ -110,7 +110,7 @@ save('test_matrix.mat','weighting_matrix')
@#for mommethod in ["GMM", "SMM"]
method_of_moments(
- % Necessery options
+ % Necessary options
mom_method = @{mommethod} % method of moments method; possible values: GMM|SMM
, datafile = 'RBC_MoM_data_@{orderApp}.mat' % name of filename with data