diff --git a/matlab/+mom/data_moments.m b/matlab/+mom/data_moments.m
index 8845f5508..140262108 100644
--- a/matlab/+mom/data_moments.m
+++ b/matlab/+mom/data_moments.m
@@ -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
diff --git a/matlab/+mom/default_option_mom_values.m b/matlab/+mom/default_option_mom_values.m
new file mode 100644
index 000000000..dd504c631
--- /dev/null
+++ b/matlab/+mom/default_option_mom_values.m
@@ -0,0 +1,269 @@
+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 M_: [structure] information about model with
+% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
+% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
+% -------------------------------------------------------------------------
+% 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 .
+% =========================================================================
+
+
+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/objective_function.m b/matlab/+mom/objective_function.m
index 13a301d7d..f83c4ef7d 100644
--- a/matlab/+mom/objective_function.m
+++ b/matlab/+mom/objective_function.m
@@ -62,7 +62,7 @@ function [fval, info, exit_flag, df, junk1, oo_, M_, options_mom_] = objective_f
% 0. 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.vector_output == 1
if options_mom_.mom.penalized_estimator
df = nan(size(oo_.mom.data_moments,1)+length(xparam1),length(xparam1));
else
@@ -88,7 +88,7 @@ M_ = set_all_parameters(xparam1, estim_params_, M_);
[fval,info,exit_flag]=check_bounds_and_definiteness_estimation(xparam1, 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
@@ -110,7 +110,7 @@ if info(1)
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
@@ -118,7 +118,7 @@ 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
@@ -151,16 +151,16 @@ 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,:);
@@ -168,8 +168,8 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
end
% 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
if options_mom_.prefilter
@@ -221,7 +221,7 @@ elseif strcmp(options_mom_.mom.mom_method,'SMM')
y_sim = simult_(M_, options_mom_, dr.ys, dr, scaled_shock_series, options_mom_.order);
% provide meaningful penalty if data is nan or inf
if any(any(isnan(y_sim))) || any(any(isinf(y_sim)))
- if options_mom_.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;
@@ -229,14 +229,14 @@ 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)';
+ 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
@@ -260,7 +260,7 @@ end
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
+if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = residuals;
if options_mom_.mom.penalized_estimator
fval=[fval;(xparam1-oo_.prior.mean)./sqrt(diag(oo_.prior.variance))];
@@ -281,7 +281,7 @@ if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
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.vector_output == 1 % lsqnonlin requires vector output
if options_mom_.mom.penalized_estimator
df(:,jp)=[dresiduals;dxparam1(:,jp)./sqrt(diag(oo_.prior.variance))];
else
diff --git a/matlab/+mom/run.m b/matlab/+mom/run.m
index 8397941c1..db6127fd0 100644
--- a/matlab/+mom/run.m
+++ b/matlab/+mom/run.m
@@ -122,6 +122,56 @@ function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_,
% - 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)
+
+
+% -------------------------------------------------------------------------
+% 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!')
+ else
+ error('method_of_moments: The ''estimated_params'' block must not be empty!')
+ end
+end
+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(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 specifiy them as a structural shock!')
+end
+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
+ 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);
+
+
+% -------------------------------------------------------------------------
+% options_mom_ structure
+% -------------------------------------------------------------------------
+% 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);
+
+
+% -------------------------------------------------------------------------
+% 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,
@@ -138,291 +188,71 @@ if isfield(options_mom_, 'TeX') && options_mom_.TeX && ~isoctave && matlab_ver_l
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
-% -------------------------------------------------------------------------
-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')
- else
- 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')
-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.')
-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')
-else
- options_mom_.logged_steady_state = 0;
- options_mom_.loglinear = false;
-end
-
-fprintf('\n==== Method of Moments Estimation (%s) ====\n\n',options_mom_.mom.mom_method)
-
-% -------------------------------------------------------------------------
-% Step 1a: Prepare options_mom_ structure
-% -------------------------------------------------------------------------
-% 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.
-
-% Method of Moments estimation options that can be set by the user in the mod file, otherwise default values are provided
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))1
- error(['method_of_moments: A variable cannot be declared as observed more than once (' options_mom_.varobs{i} ')!'])
- end
- end
- end
-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
-% -------------------------------------------------------------------------
-% 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)))];
+% 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
% -------------------------------------------------------------------------
@@ -493,23 +323,18 @@ options_mom_.mom.mom_nbr = size(M_.matched_moments,1);
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);
+not_observed_variables=setdiff(oo_.dr.inv_order_var([M_.matched_moments{:,1}]),oo_.mom.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
[xparam0, estim_params_, bayestopt_, lb, ub, M_] = set_prior(estim_params_, M_, options_mom_);
-% 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');
@@ -750,7 +575,7 @@ end
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)
+if all(abs(oo_.steady_state(oo_.dr.order_var(oo_.mom.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;
@@ -811,13 +636,13 @@ if options_mom_.mom.penalized_estimator
fprintf('\n - penalized estimation using deviation from prior mean and weighted with prior precision');
end
-for i = 1:length(optimizer_vec)
+for i = 1:length(options_mom_.optimizer_vec)
if i == 1
str = '- optimizer (mode_compute';
else
str = ' (additional_optimizer_steps';
end
- switch optimizer_vec{i}
+ switch options_mom_.optimizer_vec{i}
case 0
fprintf('\n %s=0): no minimization',str);
case 1