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@ -1,20 +1,20 @@
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function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_, estim_params_, M_, matched_moments_, options_mom_)
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%function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_, estim_params_, M_, matched_moments_, options_mom_)
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% -------------------------------------------------------------------------
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% This function performs a method of moments estimation with the following steps:
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% Step 0: Check if required structures and options exist
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% Step 1: - Prepare options_mom_ structure
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% - Carry over Options from the preprocessor
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% - Other options that need to be initialized
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% - Carry over options from the preprocessor
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% - Initialize other options
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% - Get variable orderings and state space representation
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% Step 2: Checks and transformations for matched moments structure (preliminary)
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% Step 2: Checks and transformations for matched moments structure
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% Step 3: Checks and transformations for estimated parameters, priors, and bounds
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% Step 4: Checks and transformations for data
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% Step 5: checks for steady state at initial parameters
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% Step 6: checks for objective function at initial parameters
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% Step 7: Method of moments estimation: print some info, first-stage, and second-stage
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% Step 8: Clean up
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% Step 5: Checks for steady state at initial parameters
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% Step 6: Checks for objective function at initial parameters
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% Step 7: Iterated method of moments estimation
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% Step 8: J-Test
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% Step 9: Clean up
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% -------------------------------------------------------------------------
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% This function is inspired by replication codes accompanied to the following papers:
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% 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.
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@ -35,7 +35,7 @@ function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_,
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% -------------------------------------------------------------------------
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% OUTPUTS
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% o oo_: [structure] storage for results (oo_)
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% o options_mom_: [structure] information about all used settings used in estimation (options_mom_)
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% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
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% -------------------------------------------------------------------------
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% This function is called by
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% o driver.m
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@ -47,8 +47,13 @@ function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_,
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% o dynare_minimize_objective.m
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% o evaluate_steady_state
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% o get_all_parameters.m
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% o get_matrix_entries_for_psd_check.m
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% o makedataset.m
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% o method_of_moments_data_moments.m
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% o method_of_moments_mode_check.m
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% o method_of_moments_objective_function.m
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% o method_of_moments_optimal_weighting_matrix
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% o method_of_moments_standard_errors
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% o plot_priors.m
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% o print_info.m
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% o prior_bounds.m
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@ -93,7 +98,9 @@ function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_,
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% - [ ] provide option to use analytical derivatives to compute std errors (similar to what we already do in identification)
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% - [ ] add Bayesian GMM/SMM estimation
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% - [ ] useautocorr
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% - [ ] do we need dirname?
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% - [ ] decide on default weighting matrix scheme, I would propose 2 stage with Diagonal of optimal matrix
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% - [ ] check smm with product moments greater than 2
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% -------------------------------------------------------------------------
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% Step 0: Check if required structures and options exist
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% -------------------------------------------------------------------------
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@ -132,14 +139,15 @@ fprintf('\n==== Method of Moments (%s) Estimation ====\n\n',options_mom_.mom.mom
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% Method of Moments estimation options that can be set by the user in the mod file, otherwise default values are provided
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if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
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options_mom_.mom = set_default_option(options_mom_.mom,'bartlett_kernel_lag',20); % bandwith in optimal weighting matrix
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options_mom_.mom = set_default_option(options_mom_.mom,'penalized_estimator',false); % @wmutschl: provide description
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options_mom_.mom = set_default_option(options_mom_.mom,'bartlett_kernel_lag',20); % bandwith in optimal weighting matrix
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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
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options_mom_.mom = set_default_option(options_mom_.mom,'verbose',false); % display and store intermediate estimation results
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options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix','identity_matrix'); % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
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options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix_scaling_factor',1000); % scaling of weighting matrix
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options_mom_.mom = set_default_option(options_mom_.mom,'se_tolx',1e-5); % step size for standard error
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options_mom_ = set_default_option(options_mom_,'order',1); % order of Taylor approximation in perturbation
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options_mom_ = set_default_option(options_mom_,'pruning',true); % use pruned state space system at higher-order
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options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix',{'DIAGONAL'; 'DIAGONAL'}); % weighting matrix in moments distance objective function at each iteration of estimation; cell of strings with
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% possible values are 'OPTIMAL', 'IDENTITY_MATRIX' ,'DIAGONAL' or a filename. Size of cell determines stages in iterated estimation.
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options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix_scaling_factor',1); % scaling of weighting matrix
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options_mom_.mom = set_default_option(options_mom_.mom,'se_tolx',1e-5); % step size for numerical computation of standard errors
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options_mom_ = set_default_option(options_mom_,'order',1); % order of Taylor approximation in perturbation
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options_mom_ = set_default_option(options_mom_,'pruning',true); % use pruned state space system at higher-order
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% Checks for perturbation order
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if options_mom_.order < 1
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error('method_of_moments:: The order of the Taylor approximation cannot be 0!')
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@ -147,12 +155,12 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
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end
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if strcmp(options_mom_.mom.mom_method,'SMM')
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options_mom_.mom = set_default_option(options_mom_.mom,'burnin',500); % number of periods dropped at beginning of simulation
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options_mom_.mom = set_default_option(options_mom_.mom,'bounded_shock_support',false); % trim shocks in simulation to +- 2 stdev
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options_mom_.mom = set_default_option(options_mom_.mom,'seed',24051986); % seed used in simulations
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options_mom_.mom = set_default_option(options_mom_.mom,'simulation_multiple',5); % multiple of the data length used for simulation
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options_mom_.mom = set_default_option(options_mom_.mom,'bounded_shock_support',false); % trim shocks in simulation to +- 2 stdev
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options_mom_.mom = set_default_option(options_mom_.mom,'seed',24051986); % seed used in simulations
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options_mom_.mom = set_default_option(options_mom_.mom,'simulation_multiple',5); % multiple of the data length used for simulation
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if options_mom_.mom.simulation_multiple < 1
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fprintf('The simulation horizon is shorter than the data. Dynare resets the simulation_multiple to 2.\n')
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options_mom_.mom.simulation_multiple = 2;
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fprintf('The simulation horizon is shorter than the data. Dynare resets the simulation_multiple to 5.\n')
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options_mom_.mom.simulation_multiple = 5;
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end
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end
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if strcmp(options_mom_.mom.mom_method,'GMM')
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@ -162,9 +170,10 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
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options_mom_.pruning = true;
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end
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end
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% General options that can be set by the user in the mod file, otherwise default values are provided
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options_mom_ = set_default_option(options_mom_,'dirname',M_.fname); % directory in which to store estimation output
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options_mom_ = set_default_option(options_mom_,'dirname',M_.fname); % directory in which to store estimation output
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options_mom_ = set_default_option(options_mom_,'graph_format','eps'); % specify the file format(s) for graphs saved to disk
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options_mom_ = set_default_option(options_mom_,'nodisplay',false); % do not display the graphs, but still save them to disk
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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)
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@ -196,13 +205,13 @@ options_mom_ = set_default_option(options_mom_,'optim_opt',[]);
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options_mom_ = set_default_option(options_mom_,'silent_optimizer',false); % run minimization of moments distance silently without displaying results or saving files in between
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% Mode_check plot options that can be set by the user in the mod file, otherwise default values are provided
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options_mom_.mode_check.nolik = false; % we don't do likelihood (also this initializes mode_check substructure)
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options_mom_.mode_check = set_default_option(options_mom_.mode_check,'status',true); % plot the target function for values around the computed mode for each estimated parameter in turn. This is helpful to diagnose problems with the optimizer.
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options_mom_.mode_check = set_default_option(options_mom_.mode_check,'status',false); % plot the target function for values around the computed mode for each estimated parameter in turn. This is helpful to diagnose problems with the optimizer.
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options_mom_.mode_check = set_default_option(options_mom_.mode_check,'neighbourhood_size',.5); % width of the window around the mode 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
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options_mom_.mode_check = set_default_option(options_mom_.mode_check,'symmetric_plots',true); % ensure that the check plots are symmetric around the mode. A value of 0 allows to have asymmetric plots, which can be useful if the posterior mode is close to a domain boundary, or in conjunction with mode_check_neighbourhood_size = Inf when the domain is not the entire real line
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options_mom_.mode_check = set_default_option(options_mom_.mode_check,'number_of_points',20); % number of points around the mode where the target function is evaluated (for each parameter)
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% Numerical algorithms options that can be set by the user in the mod file, otherwise default values are provided
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options_mom_ = set_default_option(options_mom_,'aim_solver',false); % Use AIM algorithm to compute perturbation approximation
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options_mom_ = set_default_option(options_mom_,'aim_solver',false); % use AIM algorithm to compute perturbation approximation instead of mjdgges
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options_mom_ = set_default_option(options_mom_,'k_order_solver',false); % use k_order_perturbation instead of mjdgges
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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
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options_mom_ = set_default_option(options_mom_,'dr_cycle_reduction_tol',1e-7); % convergence criterion used in the cycle reduction algorithm
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@ -225,15 +234,15 @@ if options_mom_.order > 2
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end
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% -------------------------------------------------------------------------
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% Step 1b: Options that are set by the preprocessor and (probably) need to be carried over
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% Step 1b: Options that are set by the preprocessor and need to be carried over
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% -------------------------------------------------------------------------
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% options related to VAROBS
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if ~isfield(options_,'varobs')
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error('method_of_moments: VAROBS statement is missing!')
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else
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options_mom_.varobs = options_.varobs; % observable variables in declaration order
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options_mom_.obs_nbr = length(options_mom_.varobs);% number of observed variables
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options_mom_.varobs = options_.varobs; % observable variables in declaration order
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options_mom_.obs_nbr = length(options_mom_.varobs); % number of observed variables
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% Check that each declared observed variable is also an endogenous variable
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for i = 1:options_mom_.obs_nbr
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if ~any(strcmp(options_mom_.varobs{i},M_.endo_names))
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@ -241,7 +250,7 @@ else
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end
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end
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% Check that a variable is not declared as observed more than once.
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% Check that a variable is not declared as observed more than once
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if length(unique(options_mom_.varobs))<length(options_mom_.varobs)
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for i = 1:options_mom_.obs_nbr
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if sum(strcmp(options_mom_.varobs{i},options_mom_.varobs))>1
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@ -259,13 +268,13 @@ end
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% options related to estimated_params and estimated_params_init
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options_mom_.use_calibration_initialization = options_.use_calibration_initialization;
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% options related to model; block
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% options related to model block
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options_mom_.linear = options_.linear;
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options_mom_.use_dll = options_.use_dll;
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options_mom_.block = options_.block;
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options_mom_.bytecode = options_.bytecode;
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% options related to steady; command
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% options related to steady command
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options_mom_.homotopy_force_continue = options_.homotopy_force_continue;
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options_mom_.homotopy_mode = options_.homotopy_mode;
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options_mom_.homotopy_steps = options_.homotopy_steps;
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@ -281,7 +290,7 @@ options_mom_.steadystate_flag = options_.steadystate_flag;
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options_mom_.dataset = options_.dataset;
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options_mom_.initial_period = options_.initial_period;
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% options related to endogenous prior restrictions
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% options related to endogenous prior restrictions are not supported
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options_mom_.endogenous_prior_restrictions.irf = {};
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options_mom_.endogenous_prior_restrictions.moment = {};
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if ~isempty(options_.endogenous_prior_restrictions.irf) && ~isempty(options_.endogenous_prior_restrictions.moment)
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@ -368,7 +377,7 @@ for jm=1:size(matched_moments_,1)
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% higher-order product moments not supported yet for GMM
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if strcmp(options_mom_.mom.mom_method, 'GMM') && sum(matched_moments_{jm,3}) > 2
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error('method_of_moments: GMM does not yet support product moments higher than 2. Change row %d in ''matched_moments'' block.',jm);
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end
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end
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% Check if declared variables are also observed (needed as otherwise the dataset variables won't coincide)
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if any(~ismember(oo_.dr.inv_order_var(matched_moments_{jm,1})', oo_.dr.obs_var))
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error('method_of_moments: Variables in row %d in ''matched_moments'' block need to be declared as VAROBS.', jm)
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@ -448,7 +457,7 @@ options_mom_.ar = max(cellfun(@max,matched_moments_(:,2))) - min(cellfun(@min,ma
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[xparam0, estim_params_, bayestopt_, lb, ub, M_] = set_prior(estim_params_, M_, options_mom_);
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% Check measurement errors
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if (estim_params_.nvn || estim_params_.ncn) && strcmp(options_mom_.mom.mom_method, 'GMM')
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if (estim_params_.nvn || estim_params_.ncn) && strcmp(options_mom_.mom.mom_method, 'GMM')
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error('method_of_moments: GMM estimation does not support measurement error(s) yet. Please specifiy them as a structural shock.')
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end
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@ -493,7 +502,11 @@ if any(bayestopt_laplace.pshape > 0) % prior specified, not ML
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end
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fprintf('The parameter(s) %s have infinite prior variance. This implies a flat prior\n',disp_string)
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fprintf('Dynare disables the matrix singularity warning\n')
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warning('off','MATLAB:singularMatrix');
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if isoctave
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warning('off','Octave:singular-matrix');
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else
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warning('off','MATLAB:singularMatrix');
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end
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end
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end
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@ -515,7 +528,7 @@ if options_mom_.use_calibration_initialization %set calibration as starting valu
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end
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end
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% Check whether on initialization
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% Check initialization
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if ~isempty(bayestopt_laplace) && all(bayestopt_laplace.pshape==0) && any(isnan(xparam0))
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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)
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end
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@ -536,6 +549,10 @@ else % estimated parameters but no declared priors
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% with inequality constraints for the parameters.
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Bounds.lb = lb;
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Bounds.ub = ub;
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if options_mom_.mom.penalized_estimator
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fprintf('Penalized estimation turned off as you did not declare priors\n')
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options_mom_.mom.penalized_estimator = 0;
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end
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end
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% Set correct bounds for standard deviations and corrrelations
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param_of_interest=(1:length(xparam0))'<=estim_params_.nvx+estim_params_.nvn;
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@ -597,9 +614,9 @@ if ~isempty(dataset_)
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options_mom_.nobs = dataset_.nobs;
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end
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% missing observations are not supported yet
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% provide info on missing observations
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if any(any(isnan(dataset_.data)))
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error('method_of_moments: missing observations are not supported')
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fprintf('missing observations will be replaced by the sample mean of the corresponding moment')
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end
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% Check length of data for estimation of second moments
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@ -732,7 +749,7 @@ else
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fprintf('\n - uncentered moments (prefilter=0)');
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end
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if options_mom_.mom.penalized_estimator
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fprintf('\n - penalized estimation using priors');
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fprintf('\n - penalized estimation using deviation from prior mean and weighted with prior precision');
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end
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if options_mom_.mode_compute == 1; fprintf('\n - optimizer (mode_compute=1): fmincon');
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elseif options_mom_.mode_compute == 2; fprintf('\n - optimizer (mode_compute=2): continuous simulated annealing');
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@ -764,89 +781,84 @@ fprintf('\n - number of matched moments: %d', options_mom_.mom.mom_nbr);
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fprintf('\n - number of parameters: %d\n\n', length(xparam0));
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% -------------------------------------------------------------------------
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% Step 7b: Method of moments estimation: First-stage
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% Step 7b: Iterated method of moments estimation
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% -------------------------------------------------------------------------
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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)))
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fprintf('\nYou did not specify the use of an optimal or diagonal covariance matrix. There is no point in running an iterated method of moments.\n')
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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')
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end
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for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
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Woptflag = 0;
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optimizer_vec=[options_mom_.mode_compute,options_mom_.additional_optimizer_steps]; % at each stage one can possibly use different optimizers sequentially
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for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
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fprintf('Estimation stage %u\n',stage_iter);
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Woptflag = false;
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switch lower(options_mom_.mom.weighting_matrix{stage_iter})
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case 'identity_matrix'
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fprintf(' - identity weighting matrix\n');
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oo_.mom.Sw = eye(options_mom_.mom_nbr);
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weighting_matrix = eye(options_mom_.mom.mom_nbr);
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case 'diagonal'
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%@wmutschl: better description in fprintf
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fprintf(' - diagonal weighting matrix: diagonal of Newey-West estimator with lag order %d\n', options_mom_.mom.bartlett_kernel_lag);
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fprintf(' and data moments as estimate of unconditional moments\n');
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W_opt = method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag);
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oo_.mom.Sw = chol(diag(diag(W_opt)));
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fprintf(' - diagonal of optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
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if stage_iter == 1
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fprintf(' and using data-moments as initial estimate of model-moments\n');
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weighting_matrix = diag(diag( method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag) ));
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else
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fprintf(' and using previous stage estimate of model-moments\n');
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weighting_matrix = diag(diag( method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag) ));
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end
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case 'optimal'
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%@wmutschl: better description in fprintf
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fprintf(' - weighting matrix: optimal. At first-stage we use diagonal of Newey-West estimator with lag order %d\n', options_mom_.mom.bartlett_kernel_lag);
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fprintf(' and the data moments as initial estimate of unconditional moments\n');
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W_opt = method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag);
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oo_.mom.Sw = chol(diag(diag(W_opt)));
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Woptflag = 1;
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fprintf(' - optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
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if stage_iter == 1
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fprintf(' and using data-moments as initial estimate of model-moments\n');
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weighting_matrix = method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag);
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else
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fprintf(' and using previous stage estimate of model-moments\n');
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weighting_matrix = method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
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Woptflag = true;
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end
|
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otherwise %user specified matrix in file
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fprintf(' - weighting matrix: user-specified\n');
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fprintf(' - user-specified weighting matrix\n');
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try
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load(options_mom_.mom.weighting_matrix{stage_iter},'weighting_matrix')
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catch
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error(['method_of_moments: No matrix named ''weighting_matrix'' could be found in ',options_mom_.mom.weighting_matrix{stage_iter},'.mat'])
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|
end
|
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|
[nrow, ncol] = size(weighting_matrix);
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|
if ~isequal(nrow,ncol) && ~isequal(nrow,length(oo_.mom.data_moments)) %check if square and right size
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|
if ~isequal(nrow,ncol) || ~isequal(nrow,length(oo_.mom.data_moments)) %check if square and right size
|
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|
error(['method_of_moments: weighting_matrix must be square and have ',num2str(length(oo_.mom.data_moments)),' rows and columns'])
|
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|
|
|
end
|
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|
|
try %check for positive definiteness
|
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|
|
oo_.Sw = chol(weighting_matrix);
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|
|
catch
|
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|
|
error('method_of_moments: Specified weighting_matrix is not positive definite')
|
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|
|
|
end
|
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|
|
|
end
|
|
|
|
|
end
|
|
|
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|
|
|
|
|
|
optimizer_vec=[options_mom_.mode_compute,options_mom_.additional_optimizer_steps];
|
|
|
|
|
|
|
|
|
|
for istep= 1:length(optimizer_vec)
|
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|
|
if optimizer_vec(istep)==13
|
|
|
|
|
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)
|
|
|
|
|
if optimizer_vec(optim_iter)==13
|
|
|
|
|
options_mom_.vector_output = true;
|
|
|
|
|
else
|
|
|
|
|
options_mom_.vector_output = false;
|
|
|
|
|
end
|
|
|
|
|
[xparam1, fval, exitflag] = dynare_minimize_objective(objective_function, xparam0, optimizer_vec(istep), options_mom_, [Bounds.lb Bounds.ub], bayestopt_laplace.name, bayestopt_laplace, [],...
|
|
|
|
|
Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_);
|
|
|
|
|
[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_, matched_moments_, M_, options_mom_);
|
|
|
|
|
if options_mom_.vector_output
|
|
|
|
|
fval = fval'*fval;
|
|
|
|
|
end
|
|
|
|
|
fprintf('\nStage %d Iteration %d: value of minimized moment distance objective function: %12.10f.\n',stage_iter,istep,fval)
|
|
|
|
|
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 (FIRST-STAGE ITERATION %d) verbose',options_mom_.mom.mom_method,istep),sprintf('verbose_%s_1st_stage_iter_%d',lower(options_mom_.mom.mom_method),istep));
|
|
|
|
|
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 oo_.mom.model_moments)
|
|
|
|
|
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_);
|
|
|
|
|
[fval, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_);
|
|
|
|
|
|
|
|
|
|
% Compute Standard errors
|
|
|
|
|
SE = method_of_moments_standard_errors(xparam1, objective_function, Bounds, oo_, estim_params_, matched_moments_, M_, options_mom_, Woptflag);
|
|
|
|
|
|
|
|
|
|
% Store first-stage results in output structure
|
|
|
|
|
% 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
|
|
|
|
|
|
|
|
|
|
%get optimal weighting matrix for J test, if necessary
|
|
|
|
|
if ~Woptflag
|
|
|
|
|
W_opt = method_of_moments_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_, matched_moments_, M_, options_mom_);
|
|
|
|
|
end
|
|
|
|
|
|
|
|
|
|
% -------------------------------------------------------------------------
|
|
|
|
|
@ -874,6 +886,9 @@ if options_mom_.mom.mom_nbr > length(xparam1)
|
|
|
|
|
fprintf('p-value of J-test statistic: %f\n',oo_.mom.J_test.p_val)
|
|
|
|
|
end
|
|
|
|
|
|
|
|
|
|
% -------------------------------------------------------------------------
|
|
|
|
|
% Step 9: Display estimation results
|
|
|
|
|
% -------------------------------------------------------------------------
|
|
|
|
|
title = ['Data moments and model moments (',options_mom_.mom.mom_method,')'];
|
|
|
|
|
headers = {'Moment','Data','Model','% dev. target'};
|
|
|
|
|
labels= matched_moments_(:,4);
|
|
|
|
|
@ -893,7 +908,11 @@ end
|
|
|
|
|
fprintf('\n==== Method of Moments Estimation (%s) Completed ====\n\n',options_mom_.mom.mom_method)
|
|
|
|
|
|
|
|
|
|
% -------------------------------------------------------------------------
|
|
|
|
|
% Step 8: Clean up
|
|
|
|
|
% Step 9: Clean up
|
|
|
|
|
% -------------------------------------------------------------------------
|
|
|
|
|
% restore warnings
|
|
|
|
|
warning('on','MATLAB:singularMatrix');
|
|
|
|
|
%reset warning state
|
|
|
|
|
if isoctave
|
|
|
|
|
warning('on')
|
|
|
|
|
else
|
|
|
|
|
warning on
|
|
|
|
|
end
|
|
|
|
|
|
Willi Mutschler
Johannes Pfeifer