📄 Updated code comments
Willi Mutschler
parent
a62e69cf39
commit
46c4dea559
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@ -1,17 +1,14 @@
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function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = dynare_identification(options_ident, pdraws0)
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%function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = dynare_identification(options_ident, pdraws0)
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% -------------------------------------------------------------------------
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% This function is called, when the user specifies identification(...); in
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% the mod file. It prepares all identification analysis, i.e.
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% (1) sets options, local and persistent variables for a new identification
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% analysis either for a single point or a MC Sample. It also displays
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% and plots the results.
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% or this function can be used to
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% This function is called, when the user specifies identification(...); in the mod file. It prepares all identification analysis:
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% (1) set options, local and persistent variables for a new identification
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% analysis either for a single point or a MC Sample. It also displays and plots the results
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% (2) load, display and plot a previously saved identification analysis
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% Note: This function does not output the arguments to the workspace if only called by
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% "identification" in the mod file, but saves results to the folder identification.
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% If you want to use this function directly in the mod file and workspace, you still have
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% to put identification once in the mod file, otherwise the preprocessor won't compute all necessary objects
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%
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% Note 1: This function does not output the arguments to the workspace, but saves results to the folder identification
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% Note 2: If you want to use this function directly in the mod file and workspace, you still have
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% to put identification in your mod file, otherwise the preprocessor won't provide all necessary objects
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% =========================================================================
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% INPUTS
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% * options_ident [structure] identification options
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@ -49,7 +46,7 @@ function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, ST
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% * skipline
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% * vnorm
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% =========================================================================
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% Copyright (C) 2010-2019 Dynare Team
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% Copyright (C) 2010-2020 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -92,11 +89,11 @@ else
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warning('off','MATLAB:log:logOfZero');
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end
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%% Set all options and create objects
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%% Set all options in options_ident and create objects
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options_ident = set_default_option(options_ident,'gsa_sample_file',0);
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% 0: do not use sample file.
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% 1: triggers gsa prior sample.
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% 2: triggers gsa Monte-Carlo sample (i.e. loads a sample corresponding to pprior=0 and ppost=0 in dynare_sensitivity options).
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% 0: do not use sample file
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% 1: triggers gsa prior sample
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% 2: triggers gsa Monte-Carlo sample (i.e. loads a sample corresponding to pprior=0 and ppost=0 in dynare_sensitivity options)
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% FILENAME: use sample file in provided path
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options_ident = set_default_option(options_ident,'parameter_set','prior_mean');
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% 'calibration': use values in M_.params and M_.Sigma_e to update estimated stderr, corr and model parameters (get_all_parameters)
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@ -108,8 +105,8 @@ options_ident = set_default_option(options_ident,'parameter_set','prior_mean');
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options_ident = set_default_option(options_ident,'load_ident_files',0);
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% 1: load previously computed analysis from identification/fname_identif.mat
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options_ident = set_default_option(options_ident,'useautocorr',0);
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% 1: use autocorrelations in Iskrev (2010)'s criteria
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% 0: use autocovariances in Iskrev (2010)'s criteria
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% 1: use autocorrelations in Iskrev (2010)'s theoretical second moments criteria
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% 0: use autocovariances in Iskrev (2010)'s theoretical second moments criteria
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options_ident = set_default_option(options_ident,'ar',1);
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% number of lags to consider for autocovariances/autocorrelations in Iskrev (2010)'s criteria
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options_ident = set_default_option(options_ident,'prior_mc',1);
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@ -118,9 +115,9 @@ options_ident = set_default_option(options_ident,'prior_range',0);
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% 1: sample uniformly from prior ranges implied by the prior specifications (overwrites prior shape when prior_mc > 1)
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% 0: sample from specified prior distributions (when prior_mc > 1)
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options_ident = set_default_option(options_ident,'periods',300);
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% length of stochastic simulation to compute simulated moment uncertainty, when analytic Hessian is not available
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% length of stochastic simulation to compute simulated moment uncertainty (when analytic Hessian is not available)
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options_ident = set_default_option(options_ident,'replic',100);
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% number of replicas to compute simulated moment uncertainty, when analytic Hessian is not available
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% number of replicas to compute simulated moment uncertainty (when analytic Hessian is not available)
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options_ident = set_default_option(options_ident,'advanced',0);
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% 1: show a more detailed analysis based on reduced-form model solution and dynamic model derivatives. Further, performs a brute force
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% search of the groups of parameters best reproducing the behavior of each single parameter.
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@ -128,8 +125,7 @@ options_ident = set_default_option(options_ident,'normalize_jacobians',1);
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% 1: normalize Jacobians by either rescaling each row by its largest element in absolute value or for Gram (or Hessian-type) matrices by transforming into correlation-type matrices
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options_ident = set_default_option(options_ident,'grid_nbr',5000);
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% number of grid points in [-pi;pi] to approximate the integral to compute Qu and Tkachenko (2012)'s criteria
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% note that grid_nbr needs to be even and actually we use (grid_nbr+1) points, as we add the 0 frequency and use symmetry, i.e. grid_nbr/2
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% negative as well as grid_nbr/2 positive values to speed up the compuations
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% note that grid_nbr needs to be even and actually we use (grid_nbr+1) points, as we add the 0 frequency and use symmetry
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if mod(options_ident.grid_nbr,2) ~= 0
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options_ident.grid_nbr = options_ident.grid_nbr+1;
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fprintf('IDENTIFICATION: ''grid_nbr'' needs to be even, hence it is reset to %d\n',options_ident.grid_nbr)
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@ -143,7 +139,7 @@ options_ident = set_default_option(options_ident,'tol_deriv',1.e-8);
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% tolerance level for selecting columns of non-zero derivatives
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options_ident = set_default_option(options_ident,'tol_sv',1.e-3);
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% tolerance level for selecting non-zero singular values in identification_checks.m
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%check whether to compute identification strength based on information matrix
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if ~isfield(options_ident,'no_identification_strength')
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options_ident.no_identification_strength = 0;
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@ -175,20 +171,20 @@ else
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options_ident.no_identification_spectrum = 1;
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end
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%overwrite setting, as identification strength and advanced need criteria based on both reducedform and moments
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%overwrite setting, as identification strength and advanced need both reducedform and moments criteria
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if (isfield(options_ident,'no_identification_strength') && options_ident.no_identification_strength == 0) || (options_ident.advanced == 1)
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options_ident.no_identification_reducedform = 0;
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options_ident.no_identification_moments = 0;
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end
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%overwrite setting, as dynare_sensitivity does not make use of spectrum and minimal system (yet)
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%overwrite setting, as dynare_sensitivity does not make use of spectrum and minimal system
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if isfield(options_,'opt_gsa') && isfield(options_.opt_gsa,'identification') && options_.opt_gsa.identification == 1
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options_ident.no_identification_minimal = 1;
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options_ident.no_identification_spectrum = 1;
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end
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%Deal with non-stationary cases
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if isfield(options_ident,'diffuse_filter') %set lik_init and options_
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if isfield(options_ident,'diffuse_filter')
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options_ident.lik_init=3; %overwrites any other lik_init set
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options_.diffuse_filter=options_ident.diffuse_filter; %make options_ inherit diffuse filter; will overwrite any conflicting lik_init in dynare_estimation_init
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else
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@ -216,7 +212,7 @@ options_ident = set_default_option(options_ident,'analytic_derivation',1);
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options_ident = set_default_option(options_ident,'order',1);
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% 1: first-order perturbation approximation, identification is based on linear state space system
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% 2: second-order perturbation approximation, identification is based on second-order pruned state space system
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% 3: third-order perturbation approximation, identification is based on third-order pruned state space system
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% 3: third-order perturbation approximation, identification is based on third-order pruned state space system
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%overwrite values in options_, note that options_ is restored at the end of the function
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if isfield(options_ident,'TeX')
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@ -302,14 +298,14 @@ options_.plot_priors = 0;
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options_.smoother = 1;
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options_.options_ident = [];
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[dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_, bayestopt_, bounds] = dynare_estimation_init(M_.endo_names, fname, 1, M_, options_, oo_, estim_params_, bayestopt_);
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%outputting dataset_ is needed for Octave
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% set method to compute identification Jacobians (kronflag). Default:0
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options_ident = set_default_option(options_ident,'analytic_derivation_mode', options_.analytic_derivation_mode); % if not set by user, inherit default global one
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% 0: efficient sylvester equation method to compute analytical derivatives as in Ratto & Iskrev (2012)
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% 1: kronecker products method to compute analytical derivatives as in Iskrev (2010) (only for order=1)
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% -1: numerical two-sided finite difference method to compute numerical derivatives of all identification Jacobians using function identification_numerical_objective.m (previously thet2tau.m)
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% -2: numerical two-sided finite difference method to compute numerically dYss, dg1, dg2, dg3, d2Yss and d2g1, the identification Jacobians are then computed analytically as if option is set to 0
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options_.analytic_derivation_mode = options_ident.analytic_derivation_mode; %overwrite setting
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% -2: numerical two-sided finite difference method to compute numerically dYss, dg1, dg2, dg3, d2Yss and d2g1, the identification Jacobians are then computed analytically as with 0
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options_.analytic_derivation_mode = options_ident.analytic_derivation_mode; %overwrite setting in options_
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% initialize persistent variables in prior_draw
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if prior_exist
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@ -344,14 +340,14 @@ if prior_exist % use estimated_params block
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indpcorr = estim_params_.corrx(:,1:2); %values correspond to varexo declaration order, row number corresponds to order in estimated_params
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end
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totparam_nbr = length(bayestopt_.name); % number of estimated stderr, corr and model parameters as declared in estimated_params
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modparam_nbr = estim_params_.np; %number of model parameters as declared in estimated_params
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stderrparam_nbr = estim_params_.nvx; % nvx is number of stderr parameters
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corrparam_nbr = estim_params_.ncx; % ncx is number of corr parameters
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modparam_nbr = estim_params_.np; % number of model parameters as declared in estimated_params
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stderrparam_nbr = estim_params_.nvx; % number of stderr parameters
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corrparam_nbr = estim_params_.ncx; % number of corr parameters
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if estim_params_.nvn || estim_params_.ncn %nvn is number of stderr parameters and ncn is number of corr parameters of measurement innovations as declared in estimated_params
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error('Identification does not (yet) support measurement errors. Instead, define them explicitly in measurement equations in model definition.')
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error('Identification does not (yet) support measurement errors. Instead, define them explicitly as varexo and provide measurement equations in the model definition.')
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end
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name = cell(totparam_nbr,1); %initialize cell for parameter names
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name_tex = cell(totparam_nbr,1);
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name = cell(totparam_nbr,1); %initialize cell for parameter names
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name_tex = cell(totparam_nbr,1); %initialize cell for TeX parameter names
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for jj=1:totparam_nbr
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if options_.TeX
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[param_name_temp, param_name_tex_temp]= get_the_name(jj,options_.TeX,M_,estim_params_,options_);
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@ -363,9 +359,9 @@ if prior_exist % use estimated_params block
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end
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end
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else % no estimated_params block, choose all model parameters and all stderr parameters, but no corr parameters
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indpmodel = 1:M_.param_nbr; %all model parameters
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indpmodel = 1:M_.param_nbr; %all model parameters
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indpstderr = 1:M_.exo_nbr; %all stderr parameters
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indpcorr = []; %no corr parameters
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indpcorr = []; %no corr parameters
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stderrparam_nbr = M_.exo_nbr;
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corrparam_nbr = 0;
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modparam_nbr = M_.param_nbr;
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@ -375,38 +371,38 @@ else % no estimated_params block, choose all model parameters and all stderr par
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name_tex = cellfun(@(x) horzcat('$ SE_{', x, '} $'), M_.exo_names, 'UniformOutput', false);
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name_tex = vertcat(name_tex, M_.param_names_tex);
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if ~isequal(M_.H,0)
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fprintf('\ndynare_identification:: Identification does not support measurement errors (yet) and will ignore them in the following. To test their identifiability, instead define them explicitly in measurement equations in the model definition.\n')
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fprintf('\ndynare_identification:: Identification does not support measurement errors (yet) and will ignore them in the following. To test their identifiability, instead define them explicitly as varexo and provide measurement equations in the model definition.\n')
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end
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end
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options_ident.name_tex = name_tex;
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skipline()
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fprintf('======== Identification Analysis ========\n')
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fprintf('\n======== Identification Analysis ========\n')
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if options_ident.order > 1
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fprintf('Using Pruned State Space System (order=%d)\n',options_ident.order);
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fprintf('Based on Pruned State Space System (order=%d)\n',options_ident.order);
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end
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skipline()
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if totparam_nbr < 2
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options_ident.advanced = 0;
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disp('There is only one parameter to study for identitification. The advanced option is re-set to 0.')
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skipline()
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fprintf('There is only one parameter to study for identitification. The advanced option is re-set to 0.\n')
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end
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% set options_ident dependent ot totparam_nbr
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% settings dependent on number of parameters
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options_ident = set_default_option(options_ident,'max_dim_cova_group',min([2,totparam_nbr-1]));
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options_ident.max_dim_cova_group = min([options_ident.max_dim_cova_group,totparam_nbr-1]);
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% In brute force search (ident_bruteforce.m) when advanced=1 this option sets the maximum dimension of groups of parameters that best reproduce the behavior of each single model parameter
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options_ident = set_default_option(options_ident,'checks_via_subsets',0);
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% 1: uses identification_checks_via_subsets.m to compute problematic parameter combinations
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% 1: uses identification_checks_via_subsets.m to compute problematic parameter combinations
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% 0: uses identification_checks.m to compute problematic parameter combinations [default]
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options_ident = set_default_option(options_ident,'max_dim_subsets_groups',min([4,totparam_nbr-1]));
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% In identification_checks_via_subsets.m, when checks_via_subsets=1, this option sets the maximum dimension of groups of parameters for which the corresponding rank criteria is checked
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% In identification_checks_via_subsets.m, when checks_via_subsets=1, this option sets the maximum dimension of groups of parameters for which the corresponding rank criteria is checked
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options_.options_ident = options_ident; %store identification options into global options
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% store identification options
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options_.options_ident = options_ident;
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store_options_ident = options_ident;
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MaxNumberOfBytes = options_.MaxNumberOfBytes; %threshold when to save from memory to files
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% get some options for quick reference
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iload = options_ident.load_ident_files;
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SampleSize = options_ident.prior_mc;
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@ -419,43 +415,37 @@ if iload <=0
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% only bounds are specified in estimated_params
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parameters = 'ML_Starting_value';
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parameters_TeX = 'ML starting value';
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disp('Testing ML Starting value')
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fprintf('Testing ML Starting value\n');
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else
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% use user-defined option
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switch parameters
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case 'calibration'
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parameters_TeX = 'Calibration';
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disp('Testing calibration')
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params(1,:) = get_all_parameters(estim_params_,M_);
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case 'posterior_mode'
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parameters_TeX = 'Posterior mode';
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disp('Testing posterior mode')
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params(1,:) = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
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case 'posterior_mean'
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parameters_TeX = 'Posterior mean';
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disp('Testing posterior mean')
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params(1,:) = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
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case 'posterior_median'
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parameters_TeX = 'Posterior median';
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disp('Testing posterior median')
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params(1,:) = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
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case 'prior_mode'
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parameters_TeX = 'Prior mode';
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disp('Testing prior mode')
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params(1,:) = bayestopt_.p5(:);
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case 'prior_mean'
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parameters_TeX = 'Prior mean';
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disp('Testing prior mean')
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params(1,:) = bayestopt_.p1;
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otherwise
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disp('The option parameter_set has to be equal to:')
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disp(' ''calibration'', ')
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disp(' ''posterior_mode'', ')
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disp(' ''posterior_mean'', ')
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disp(' ''posterior_median'', ')
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disp(' ''prior_mode'' or')
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disp(' ''prior_mean''.')
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error('IDENTIFICATION: The option ''parameter_set'' has and invalid value');
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case 'calibration'
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parameters_TeX = 'Calibration';
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fprintf('Testing calibration\n');
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params(1,:) = get_all_parameters(estim_params_,M_);
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case 'posterior_mode'
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parameters_TeX = 'Posterior mode';
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fprintf('Testing posterior mode\n');
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params(1,:) = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
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case 'posterior_mean'
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parameters_TeX = 'Posterior mean';
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fprintf('Testing posterior mean\n');
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params(1,:) = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
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case 'posterior_median'
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parameters_TeX = 'Posterior median';
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fprintf('Testing posterior median\n');
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params(1,:) = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
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case 'prior_mode'
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parameters_TeX = 'Prior mode';
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fprintf('Testing prior mode\n');
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params(1,:) = bayestopt_.p5(:);
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case 'prior_mean'
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parameters_TeX = 'Prior mean';
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fprintf('Testing prior mean\n');
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params(1,:) = bayestopt_.p1;
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otherwise
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fprintf('The option parameter_set has to be equal to: ''calibration'', ''posterior_mode'', ''posterior_mean'', ''posterior_median'', ''prior_mode'' or ''prior_mean''.\n');
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error('IDENTIFICATION: The option ''parameter_set'' has an invalid value');
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end
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end
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else
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@ -463,7 +453,7 @@ if iload <=0
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params = [sqrt(diag(M_.Sigma_e))', M_.params'];
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parameters = 'Current_params';
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parameters_TeX = 'Current parameter values';
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disp('Testing all current stderr and model parameter values')
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fprintf('Testing all current stderr and model parameter values\n');
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end
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options_ident.tittxt = parameters; %title text for graphs and figures
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% perform identification analysis for single point
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@ -471,43 +461,13 @@ if iload <=0
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identification_analysis(params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end implies initialization of persistent variables
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if info(1)~=0
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% there are errors in the solution algorithm
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skipline()
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disp('----------- ')
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disp('Parameter error:')
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disp(['The model does not solve for ', parameters, ' with error code info = ', int2str(info(1))]),
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skipline()
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if info(1)==1
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disp('info==1 %! The model doesn''t determine the current variables uniquely.')
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elseif info(1)==2
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disp('info==2 %! MJDGGES returned an error code.')
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elseif info(1)==3
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disp('info==3 %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium. ')
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elseif info(1)==4
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disp('info==4 %! Blanchard & Kahn conditions are not satisfied: indeterminacy. ')
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elseif info(1)==5
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disp('info==5 %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure. ')
|
||||
elseif info(1)==6
|
||||
disp('info==6 %! The jacobian evaluated at the deterministic steady state is complex.')
|
||||
elseif info(1)==19
|
||||
disp('info==19 %! The steadystate routine has thrown an exception (inconsistent deep parameters). ')
|
||||
elseif info(1)==20
|
||||
disp('info==20 %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations). ')
|
||||
elseif info(1)==21
|
||||
disp('info==21 %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.')
|
||||
elseif info(1)==22
|
||||
disp('info==22 %! The steady has NaNs. ')
|
||||
elseif info(1)==23
|
||||
disp('info==23 %! M_.params has been updated in the steadystate routine and has complex valued scalars. ')
|
||||
elseif info(1)==24
|
||||
disp('info==24 %! M_.params has been updated in the steadystate routine and has some NaNs. ')
|
||||
elseif info(1)==30
|
||||
disp('info==30 %! Ergodic variance can''t be computed. ')
|
||||
end
|
||||
disp('----------- ')
|
||||
skipline()
|
||||
message = get_error_message(info,0,options_);
|
||||
fprintf('-----------\n');
|
||||
fprintf('The model does not solve for %s (info = %d: %s)\n', parameters, info(1), message);
|
||||
fprintf('-----------\n');
|
||||
if any(bayestopt_.pshape)
|
||||
% if there are errors in the solution algorithm, try to sample a different point from the prior
|
||||
disp('Try sampling up to 50 parameter sets from the prior.')
|
||||
fprintf('Try sampling up to 50 parameter sets from the prior.\n');
|
||||
kk=0;
|
||||
while kk<50 && info(1)
|
||||
kk=kk+1;
|
||||
|
|
@ -515,53 +475,50 @@ if iload <=0
|
|||
options_ident.tittxt = 'Random_prior_params'; %title text for graphs and figures
|
||||
% perform identification analysis
|
||||
[ide_moments_point, ide_spectrum_point, ide_minimal_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, derivatives_info_point, info, options_ident] = ...
|
||||
identification_analysis(params,indpmodel,indpstderr,indpcorr,options_ident,dataset_info, prior_exist, 1);
|
||||
identification_analysis(params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1);
|
||||
end
|
||||
end
|
||||
if info(1)
|
||||
skipline()
|
||||
disp('----------- ')
|
||||
disp('Identification stopped:')
|
||||
fprintf('\n-----------\n');
|
||||
fprintf('Identification stopped:\n');
|
||||
if any(bayestopt_.pshape)
|
||||
disp('The model did not solve for any of 50 attempts of random samples from the prior')
|
||||
fprintf('The model did not solve for any of 50 attempts of random samples from the prior\n');
|
||||
end
|
||||
disp('----------- ')
|
||||
skipline()
|
||||
fprintf('-----------\n');
|
||||
return
|
||||
else
|
||||
% found a (random) point that solves the model
|
||||
disp('Found a random draw from the priors that solves the model.')
|
||||
disp(params)
|
||||
disp('Identification now continues for this draw.');
|
||||
fprintf('Found a random draw from the priors that solves the model:\n');
|
||||
disp(params);
|
||||
fprintf('Identification now continues for this draw.');
|
||||
parameters = 'Random_prior_params';
|
||||
parameters_TeX = 'Random prior parameter draw';
|
||||
end
|
||||
end
|
||||
ide_hess_point.params = params;
|
||||
% save all output into identification folder
|
||||
save([IdentifDirectoryName '/' fname '_identif.mat'], 'ide_moments_point', 'ide_spectrum_point', 'ide_minimal_point', 'ide_hess_point', 'ide_reducedform_point', 'ide_dynamic_point','store_options_ident');
|
||||
save([IdentifDirectoryName '/' fname '_' parameters '_identif.mat'], 'ide_moments_point', 'ide_spectrum_point', 'ide_minimal_point', 'ide_hess_point', 'ide_reducedform_point', 'ide_dynamic_point','store_options_ident');
|
||||
save([IdentifDirectoryName '/' fname '_identif.mat'], 'ide_moments_point', 'ide_spectrum_point', 'ide_minimal_point', 'ide_hess_point', 'ide_reducedform_point', 'ide_dynamic_point', 'store_options_ident');
|
||||
save([IdentifDirectoryName '/' fname '_' parameters '_identif.mat'], 'ide_moments_point', 'ide_spectrum_point', 'ide_minimal_point', 'ide_hess_point', 'ide_reducedform_point', 'ide_dynamic_point', 'store_options_ident');
|
||||
% display results of identification analysis
|
||||
disp_identification(params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident);
|
||||
if ~options_ident.no_identification_strength && ~options_.nograph
|
||||
% plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs
|
||||
% plot (i) identification strength and sensitivity measure based on the moment information matrix and (ii) plot advanced analysis graphs
|
||||
plot_identification(params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, IdentifDirectoryName, parameters_TeX, name_tex);
|
||||
end
|
||||
|
||||
if SampleSize > 1
|
||||
% initializations for Monte Carlo Analysis
|
||||
skipline()
|
||||
disp('Monte Carlo Testing')
|
||||
fprintf('\nMonte Carlo Testing\n');
|
||||
h = dyn_waitbar(0,'Monte Carlo identification checks ...');
|
||||
iteration = 0; % initialize counter for admissable draws
|
||||
run_index = 0; % initialize counter for admissable draws after saving previous draws to file(s)
|
||||
file_index = 0; % initialize counter for files (if MaxNumberOfBytes is reached, we store results in files)
|
||||
file_index = 0; % initialize counter for files (if options_.MaxNumberOfBytes is reached, we store results in files)
|
||||
options_MC = options_ident; %store options structure for Monte Carlo analysis
|
||||
options_MC.advanced = 0; %do not run advanced checking in a Monte Carlo analysis
|
||||
options_ident.checks_via_subsets = 0; % for Monte Carlo analysis currently only identification_checks and not identification_checks_via_subsets is supported
|
||||
else
|
||||
iteration = 1; % iteration equals SampleSize and we are finished
|
||||
pdraws = []; % to have output object otherwise map_ident may crash
|
||||
pdraws = []; % to have output object otherwise map_ident.m may crash
|
||||
end
|
||||
while iteration < SampleSize
|
||||
if external_sample
|
||||
|
|
@ -576,78 +533,84 @@ if iload <=0
|
|||
|
||||
if iteration==0 && info(1)==0 % preallocate storage in the first admissable run
|
||||
delete([IdentifDirectoryName '/' fname '_identif_*.mat']) % delete previously saved results
|
||||
MAX_RUNS_BEFORE_SAVE_TO_FILE = min(SampleSize,ceil(MaxNumberOfBytes/(size(ide_reducedform.si_dREDUCEDFORM,1)*totparam_nbr)/8)); % set how many runs can be stored before we save to files
|
||||
MAX_RUNS_BEFORE_SAVE_TO_FILE = min(SampleSize,ceil(options_.MaxNumberOfBytes/(size(ide_reducedform.si_dREDUCEDFORM,1)*totparam_nbr)/8)); % set how many runs can be stored before we save to files
|
||||
pdraws = zeros(SampleSize,totparam_nbr); % preallocate storage for draws in each row
|
||||
|
||||
% preallocate storage for steady state and dynamic model derivatives
|
||||
STO_si_dDYNAMIC = zeros([size(ide_dynamic.si_dDYNAMIC,1),modparam_nbr,MAX_RUNS_BEFORE_SAVE_TO_FILE]);
|
||||
STO_DYNAMIC = zeros(size(ide_dynamic.DYNAMIC,1),SampleSize);
|
||||
IDE_DYNAMIC.ind_dDYNAMIC = ide_dynamic.ind_dDYNAMIC;
|
||||
IDE_DYNAMIC.in0 = zeros(SampleSize,modparam_nbr);
|
||||
IDE_DYNAMIC.ind0 = zeros(SampleSize,modparam_nbr);
|
||||
IDE_DYNAMIC.jweak = zeros(SampleSize,modparam_nbr);
|
||||
IDE_DYNAMIC.jweak_pair = zeros(SampleSize,modparam_nbr*(modparam_nbr+1)/2);
|
||||
IDE_DYNAMIC.cond = zeros(SampleSize,1);
|
||||
IDE_DYNAMIC.Mco = zeros(SampleSize,modparam_nbr);
|
||||
% preallocate storage for dynamic model
|
||||
STO_si_dDYNAMIC = zeros([size(ide_dynamic.si_dDYNAMIC, 1), modparam_nbr, MAX_RUNS_BEFORE_SAVE_TO_FILE]);
|
||||
STO_DYNAMIC = zeros(size(ide_dynamic.DYNAMIC, 1), SampleSize);
|
||||
IDE_DYNAMIC.ind_dDYNAMIC = ide_dynamic.ind_dDYNAMIC;
|
||||
IDE_DYNAMIC.in0 = zeros(SampleSize, modparam_nbr);
|
||||
IDE_DYNAMIC.ind0 = zeros(SampleSize, modparam_nbr);
|
||||
IDE_DYNAMIC.jweak = zeros(SampleSize, modparam_nbr);
|
||||
IDE_DYNAMIC.jweak_pair = zeros(SampleSize, modparam_nbr*(modparam_nbr+1)/2);
|
||||
IDE_DYNAMIC.cond = zeros(SampleSize, 1);
|
||||
IDE_DYNAMIC.Mco = zeros(SampleSize, modparam_nbr);
|
||||
|
||||
% preallocate storage for reduced form
|
||||
if ~options_MC.no_identification_reducedform
|
||||
STO_si_dREDUCEDFORM = zeros([size(ide_reducedform.si_dREDUCEDFORM,1),totparam_nbr,MAX_RUNS_BEFORE_SAVE_TO_FILE]);
|
||||
STO_REDUCEDFORM = zeros(size(ide_reducedform.REDUCEDFORM,1),SampleSize);
|
||||
IDE_REDUCEDFORM.ind_dREDUCEDFORM = ide_reducedform.ind_dREDUCEDFORM;
|
||||
IDE_REDUCEDFORM.in0 = zeros(SampleSize,1);
|
||||
IDE_REDUCEDFORM.ind0 = zeros(SampleSize,totparam_nbr);
|
||||
IDE_REDUCEDFORM.jweak = zeros(SampleSize,totparam_nbr);
|
||||
IDE_REDUCEDFORM.jweak_pair = zeros(SampleSize,totparam_nbr*(totparam_nbr+1)/2);
|
||||
IDE_REDUCEDFORM.cond = zeros(SampleSize,1);
|
||||
IDE_REDUCEDFORM.Mco = zeros(SampleSize,totparam_nbr);
|
||||
STO_si_dREDUCEDFORM = zeros([size(ide_reducedform.si_dREDUCEDFORM, 1), totparam_nbr, MAX_RUNS_BEFORE_SAVE_TO_FILE]);
|
||||
STO_REDUCEDFORM = zeros(size(ide_reducedform.REDUCEDFORM, 1), SampleSize);
|
||||
IDE_REDUCEDFORM.ind_dREDUCEDFORM = ide_reducedform.ind_dREDUCEDFORM;
|
||||
IDE_REDUCEDFORM.in0 = zeros(SampleSize, 1);
|
||||
IDE_REDUCEDFORM.ind0 = zeros(SampleSize, totparam_nbr);
|
||||
IDE_REDUCEDFORM.jweak = zeros(SampleSize, totparam_nbr);
|
||||
IDE_REDUCEDFORM.jweak_pair = zeros(SampleSize, totparam_nbr*(totparam_nbr+1)/2);
|
||||
IDE_REDUCEDFORM.cond = zeros(SampleSize, 1);
|
||||
IDE_REDUCEDFORM.Mco = zeros(SampleSize, totparam_nbr);
|
||||
else
|
||||
IDE_REDUCEDFORM = {};
|
||||
STO_si_dREDUCEDFORM = {};
|
||||
STO_REDUCEDFORM = {};
|
||||
IDE_REDUCEDFORM = {};
|
||||
end
|
||||
|
||||
% preallocate storage for moments
|
||||
if ~options_MC.no_identification_moments
|
||||
STO_si_dMOMENTS = zeros([size(ide_moments.si_dMOMENTS,1),totparam_nbr,MAX_RUNS_BEFORE_SAVE_TO_FILE]);
|
||||
STO_MOMENTS = zeros(size(ide_moments.MOMENTS,1),SampleSize);
|
||||
STO_si_dMOMENTS = zeros([size(ide_moments.si_dMOMENTS, 1), totparam_nbr, MAX_RUNS_BEFORE_SAVE_TO_FILE]);
|
||||
STO_MOMENTS = zeros(size(ide_moments.MOMENTS, 1), SampleSize);
|
||||
IDE_MOMENTS.ind_dMOMENTS = ide_moments.ind_dMOMENTS;
|
||||
IDE_MOMENTS.in0 = zeros(SampleSize,1);
|
||||
IDE_MOMENTS.ind0 = zeros(SampleSize,totparam_nbr);
|
||||
IDE_MOMENTS.jweak = zeros(SampleSize,totparam_nbr);
|
||||
IDE_MOMENTS.jweak_pair = zeros(SampleSize,totparam_nbr*(totparam_nbr+1)/2);
|
||||
IDE_MOMENTS.cond = zeros(SampleSize,1);
|
||||
IDE_MOMENTS.Mco = zeros(SampleSize,totparam_nbr);
|
||||
IDE_MOMENTS.S = zeros(SampleSize,min(8,totparam_nbr));
|
||||
IDE_MOMENTS.V = zeros(SampleSize,totparam_nbr,min(8,totparam_nbr));
|
||||
IDE_MOMENTS.in0 = zeros(SampleSize, 1);
|
||||
IDE_MOMENTS.ind0 = zeros(SampleSize, totparam_nbr);
|
||||
IDE_MOMENTS.jweak = zeros(SampleSize, totparam_nbr);
|
||||
IDE_MOMENTS.jweak_pair = zeros(SampleSize, totparam_nbr*(totparam_nbr+1)/2);
|
||||
IDE_MOMENTS.cond = zeros(SampleSize, 1);
|
||||
IDE_MOMENTS.Mco = zeros(SampleSize, totparam_nbr);
|
||||
IDE_MOMENTS.S = zeros(SampleSize, min(8, totparam_nbr));
|
||||
IDE_MOMENTS.V = zeros(SampleSize, totparam_nbr, min(8, totparam_nbr));
|
||||
else
|
||||
IDE_MOMENTS = {};
|
||||
STO_si_dMOMENTS = {};
|
||||
STO_MOMENTS = {};
|
||||
IDE_MOMENTS = {};
|
||||
end
|
||||
|
||||
% preallocate storage for spectrum
|
||||
if ~options_MC.no_identification_spectrum
|
||||
STO_dSPECTRUM = zeros([size(ide_spectrum.dSPECTRUM,1),size(ide_spectrum.dSPECTRUM,2), MAX_RUNS_BEFORE_SAVE_TO_FILE]);
|
||||
STO_dSPECTRUM = zeros([size(ide_spectrum.dSPECTRUM, 1), size(ide_spectrum.dSPECTRUM, 2), MAX_RUNS_BEFORE_SAVE_TO_FILE]);
|
||||
IDE_SPECTRUM.ind_dSPECTRUM = ide_spectrum.ind_dSPECTRUM;
|
||||
IDE_SPECTRUM.in0 = zeros(SampleSize,1);
|
||||
IDE_SPECTRUM.ind0 = zeros(SampleSize,totparam_nbr);
|
||||
IDE_SPECTRUM.jweak = zeros(SampleSize,totparam_nbr);
|
||||
IDE_SPECTRUM.jweak_pair = zeros(SampleSize,totparam_nbr*(totparam_nbr+1)/2);
|
||||
IDE_SPECTRUM.cond = zeros(SampleSize,1);
|
||||
IDE_SPECTRUM.Mco = zeros(SampleSize,totparam_nbr);
|
||||
IDE_SPECTRUM.in0 = zeros(SampleSize, 1);
|
||||
IDE_SPECTRUM.ind0 = zeros(SampleSize, totparam_nbr);
|
||||
IDE_SPECTRUM.jweak = zeros(SampleSize, totparam_nbr);
|
||||
IDE_SPECTRUM.jweak_pair = zeros(SampleSize, totparam_nbr*(totparam_nbr+1)/2);
|
||||
IDE_SPECTRUM.cond = zeros(SampleSize, 1);
|
||||
IDE_SPECTRUM.Mco = zeros(SampleSize, totparam_nbr);
|
||||
else
|
||||
IDE_SPECTRUM = {};
|
||||
STO_dSPECTRUM = {};
|
||||
IDE_SPECTRUM = {};
|
||||
end
|
||||
|
||||
% preallocate storage for minimal system
|
||||
if ~options_MC.no_identification_minimal
|
||||
STO_dMINIMAL = zeros([size(ide_minimal.dMINIMAL,1),size(ide_minimal.dMINIMAL,2), MAX_RUNS_BEFORE_SAVE_TO_FILE]);
|
||||
STO_dMINIMAL = zeros([size(ide_minimal.dMINIMAL, 1), size(ide_minimal.dMINIMAL, 2), MAX_RUNS_BEFORE_SAVE_TO_FILE]);
|
||||
IDE_MINIMAL.ind_dMINIMAL = ide_minimal.ind_dMINIMAL;
|
||||
IDE_MINIMAL.in0 = zeros(SampleSize,1);
|
||||
IDE_MINIMAL.ind0 = zeros(SampleSize,totparam_nbr);
|
||||
IDE_MINIMAL.jweak = zeros(SampleSize,totparam_nbr);
|
||||
IDE_MINIMAL.jweak_pair = zeros(SampleSize,totparam_nbr*(totparam_nbr+1)/2);
|
||||
IDE_MINIMAL.cond = zeros(SampleSize,1);
|
||||
IDE_MINIMAL.Mco = zeros(SampleSize,totparam_nbr);
|
||||
IDE_MINIMAL.in0 = zeros(SampleSize, 1);
|
||||
IDE_MINIMAL.ind0 = zeros(SampleSize, totparam_nbr);
|
||||
IDE_MINIMAL.jweak = zeros(SampleSize, totparam_nbr);
|
||||
IDE_MINIMAL.jweak_pair = zeros(SampleSize, totparam_nbr*(totparam_nbr+1)/2);
|
||||
IDE_MINIMAL.cond = zeros(SampleSize, 1);
|
||||
IDE_MINIMAL.Mco = zeros(SampleSize, totparam_nbr);
|
||||
else
|
||||
IDE_MINIMAL = {};
|
||||
STO_dMINIMAL = {};
|
||||
IDE_MINIMAL = {};
|
||||
end
|
||||
end
|
||||
|
||||
|
|
@ -666,10 +629,10 @@ if iload <=0
|
|||
IDE_DYNAMIC.jweak_pair(iteration,:) = ide_dynamic.jweak_pair;
|
||||
IDE_DYNAMIC.Mco(iteration,:) = ide_dynamic.Mco;
|
||||
|
||||
% store results for reduced form model solution
|
||||
% store results for reduced form
|
||||
if ~options_MC.no_identification_reducedform
|
||||
STO_REDUCEDFORM(:,iteration) = ide_reducedform.REDUCEDFORM;
|
||||
STO_si_dREDUCEDFORM(:,:,run_index) = ide_reducedform.si_dREDUCEDFORM;
|
||||
STO_REDUCEDFORM(:,iteration) = ide_reducedform.REDUCEDFORM;
|
||||
STO_si_dREDUCEDFORM(:,:,run_index) = ide_reducedform.si_dREDUCEDFORM;
|
||||
IDE_REDUCEDFORM.cond(iteration,1) = ide_reducedform.cond;
|
||||
IDE_REDUCEDFORM.ino(iteration,1) = ide_reducedform.ino;
|
||||
IDE_REDUCEDFORM.ind0(iteration,:) = ide_reducedform.ind0;
|
||||
|
|
@ -717,7 +680,7 @@ if iload <=0
|
|||
% save results to file: either to avoid running into memory issues, i.e. (run_index==MAX_RUNS_BEFORE_SAVE_TO_FILE) or if finished (iteration==SampleSize)
|
||||
if run_index==MAX_RUNS_BEFORE_SAVE_TO_FILE || iteration==SampleSize
|
||||
file_index = file_index + 1;
|
||||
if run_index<MAX_RUNS_BEFORE_SAVE_TO_FILE
|
||||
if run_index < MAX_RUNS_BEFORE_SAVE_TO_FILE
|
||||
%we are finished (iteration == SampleSize), so get rid of additional storage
|
||||
STO_si_dDYNAMIC = STO_si_dDYNAMIC(:,:,1:run_index);
|
||||
if ~options_MC.no_identification_reducedform
|
||||
|
|
@ -754,7 +717,7 @@ if iload <=0
|
|||
run_index = 0; % reset index
|
||||
end
|
||||
if SampleSize > 1
|
||||
dyn_waitbar(iteration/SampleSize,h,['MC identification checks ',int2str(iteration),'/',int2str(SampleSize)])
|
||||
dyn_waitbar(iteration/SampleSize, h, ['MC identification checks ', int2str(iteration), '/', int2str(SampleSize)]);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
|
@ -769,10 +732,10 @@ if iload <=0
|
|||
normalize_STO_MOMENTS = std(STO_MOMENTS,0,2);
|
||||
end
|
||||
if ~options_MC.no_identification_minimal
|
||||
normalize_STO_MINIMAL = 1; %not yet used
|
||||
normalize_STO_MINIMAL = 1; %not used (yet)
|
||||
end
|
||||
if ~options_MC.no_identification_spectrum
|
||||
normalize_STO_SPECTRUM = 1; %not yet used
|
||||
normalize_STO_SPECTRUM = 1; %not used (yet)
|
||||
end
|
||||
normaliz1 = std(pdraws);
|
||||
iter = 0;
|
||||
|
|
@ -854,11 +817,11 @@ end
|
|||
%% if dynare_identification is called as it own function (not through identification command) and if we load files
|
||||
if nargout>3 && iload
|
||||
filnam = dir([IdentifDirectoryName '/' fname '_identif_*.mat']);
|
||||
STO_si_dDYNAMIC = [];
|
||||
STO_si_dREDUCEDFORM=[];
|
||||
STO_si_dMOMENTS = [];
|
||||
STO_dSPECTRUM = [];
|
||||
STO_dMINIMAL = [];
|
||||
STO_si_dDYNAMIC = [];
|
||||
STO_si_dREDUCEDFORM = [];
|
||||
STO_si_dMOMENTS = [];
|
||||
STO_dSPECTRUM = [];
|
||||
STO_dMINIMAL = [];
|
||||
for j=1:length(filnam)
|
||||
load([IdentifDirectoryName '/' fname '_identif_',int2str(j),'.mat']);
|
||||
STO_si_dDYNAMIC = cat(3,STO_si_dDYNAMIC, STO_si_dDYNAMIC(:,abs(iload),:));
|
||||
|
|
@ -879,18 +842,17 @@ end
|
|||
|
||||
if iload
|
||||
%if previous analysis is loaded
|
||||
disp(['Testing ',parameters])
|
||||
fprintf(['Testing %s\n',parameters]);
|
||||
disp_identification(ide_hess_point.params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident);
|
||||
if ~options_.nograph
|
||||
% plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs
|
||||
plot_identification(ide_hess_point.params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, IdentifDirectoryName, [],name_tex);
|
||||
plot_identification(ide_hess_point.params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, IdentifDirectoryName, [], name_tex);
|
||||
end
|
||||
end
|
||||
|
||||
%displaying and plotting of results for MC sample
|
||||
if SampleSize > 1
|
||||
fprintf('\n')
|
||||
disp('Testing MC sample')
|
||||
fprintf('\nTesting MC sample\n');
|
||||
%print results to console but make sure advanced=0
|
||||
advanced0 = options_ident.advanced;
|
||||
options_ident.advanced = 0;
|
||||
|
|
@ -909,11 +871,9 @@ if SampleSize > 1
|
|||
store_nodisplay = options_.nodisplay;
|
||||
options_.nodisplay = 1;
|
||||
% HIGHEST CONDITION NUMBER
|
||||
[~, jmax] = max(IDE_MOMENTS.cond);
|
||||
fprintf('\n')
|
||||
[~, jmax] = max(IDE_MOMENTS.cond);
|
||||
tittxt = 'Draw with HIGHEST condition number';
|
||||
fprintf('\n')
|
||||
disp(['Testing ',tittxt, '.']),
|
||||
fprintf('\nTesting %s.\n',tittxt);
|
||||
if ~iload
|
||||
options_ident.tittxt = tittxt; %title text for graphs and figures
|
||||
[ide_moments_max, ide_spectrum_max, ide_minimal_max, ide_hess_max, ide_reducedform_max, ide_dynamic_max, derivatives_info_max, info_max, options_ident] = ...
|
||||
|
|
@ -930,10 +890,8 @@ if SampleSize > 1
|
|||
|
||||
% SMALLEST condition number
|
||||
[~, jmin] = min(IDE_MOMENTS.cond);
|
||||
fprintf('\n')
|
||||
tittxt = 'Draw with SMALLEST condition number';
|
||||
fprintf('\n')
|
||||
disp(['Testing ',tittxt, '.']),
|
||||
fprintf('Testing %s.\n',tittxt);
|
||||
if ~iload
|
||||
options_ident.tittxt = tittxt; %title text for graphs and figures
|
||||
[ide_moments_min, ide_spectrum_min, ide_minimal_min, ide_hess_min, ide_reducedform_min, ide_dynamic_min, derivatives_info_min, info_min, options_ident] = ...
|
||||
|
|
@ -954,9 +912,8 @@ if SampleSize > 1
|
|||
store_nodisplay = options_.nodisplay;
|
||||
options_.nodisplay = 1;
|
||||
for j=1:length(jcrit)
|
||||
tittxt = ['Rank deficient draw n. ',int2str(j)];
|
||||
fprintf('\n')
|
||||
disp(['Testing ',tittxt, '.']),
|
||||
tittxt = ['Rank deficient draw nr. ',int2str(j)];
|
||||
fprintf('\nTesting %s.\n',tittxt);
|
||||
if ~iload
|
||||
options_ident.tittxt = tittxt; %title text for graphs and figures
|
||||
[ide_moments_(j), ide_spectrum_(j), ide_minimal_(j), ide_hess_(j), ide_reducedform_(j), ide_dynamic_(j), derivatives_info_(j), info_resolve, options_ident] = ...
|
||||
|
|
@ -987,8 +944,6 @@ else
|
|||
warning on
|
||||
end
|
||||
|
||||
skipline()
|
||||
disp('==== Identification analysis completed ====')
|
||||
skipline(2)
|
||||
fprintf('\n==== Identification analysis completed ====\n\n')
|
||||
|
||||
options_ = store_options_; %restore options set
|
||||
options_ = store_options_; %restore options set
|
||||
|
|
@ -10,7 +10,7 @@ function fjac = fjaco(f,x,varargin)
|
|||
% OUTPUT
|
||||
% fjac : finite difference Jacobian
|
||||
%
|
||||
% Copyright (C) 2010-2017,2019 Dynare Team
|
||||
% Copyright (C) 2010-2017,2019-2020 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
function [E_y, dE_y, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params, M, oo, options, options_ident, indpmodel, indpstderr, indpcorr, indvobs)
|
||||
function [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params, M, oo, options, options_ident, indpmodel, indpstderr, indpcorr, indvobs)
|
||||
% function [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params, M, oo, options, options_ident, indpmodel, indpstderr, indpcorr, indvobs)
|
||||
% previously getJJ.m
|
||||
% % Sets up the Jacobians needed for identification analysis
|
||||
% previously getJJ.m in Dynare 4.5
|
||||
% Sets up the Jacobians needed for identification analysis
|
||||
% =========================================================================
|
||||
% INPUTS
|
||||
% estim_params: [structure] storing the estimation information
|
||||
|
|
@ -27,46 +27,50 @@ function [E_y, dE_y, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOM
|
|||
%
|
||||
% MEAN [endo_nbr by 1], in DR order. Expectation of all model variables
|
||||
% * order==1: corresponds to steady state
|
||||
% * order==2: computed from pruned state space system (as in e.g. Andreasen, Fernandez-Villaverde, Rubio-Ramirez, 2018)
|
||||
% * order==2|3: corresponds to mean computed from pruned state space system (as in Andreasen, Fernandez-Villaverde, Rubio-Ramirez, 2018)
|
||||
% dMEAN [endo_nbr by totparam_nbr], in DR Order, Jacobian (wrt all params) of MEAN
|
||||
%
|
||||
% REDUCEDFORM [rowredform_nbr by 1] in DR order. Steady state and reduced-form model solution matrices for all model variables
|
||||
% * order==1: [Yss' vec(dghx)' vech(dghu*Sigma_e*dghu')']',
|
||||
% where rowredform_nbr = endo_nbr+endo_nbr*nspred+endo_nbr*(endo_nbr+1)/2
|
||||
% * order==2: [Yss' vec(dghx)' vech(dghu*Sigma_e*dghu')' vec(dghxx)' vec(dghxu)' vec(dghuu)' vec(dghs2)']',
|
||||
% where rowredform_nbr = endo_nbr+endo_nbr*nspred+endo_nbr*(endo_nbr+1)/2+endo_nbr*nspred^2*endo_nbr*nspred*exo_nr+endo_nbr*exo_nbr^2+endo_nbr
|
||||
% dREDUCEDFORM: [rowrf_nbr by totparam_nbr] in DR order, Jacobian (wrt all params) of REDUCEDFORM
|
||||
% * order==1: [Yss' vec(ghx)' vech(ghu*Sigma_e*ghu')']',
|
||||
% where rowredform_nbr = endo_nbr*(1+nspred+(endo_nbr+1)/2)
|
||||
% * order==2: [Yss' vec(ghx)' vech(ghu*Sigma_e*ghu')' vec(ghxx)' vec(ghxu)' vec(ghuu)' vec(ghs2)']',
|
||||
% where rowredform_nbr = endo_nbr*(1+nspred+(endo_nbr+1)/2+nspred^2+nspred*exo_nr+exo_nbr^2+1)
|
||||
% * order==3: [Yss' vec(ghx)' vech(ghu*Sigma_e*ghu')' vec(ghxx)' vec(ghxu)' vec(ghuu)' vec(ghs2)' vec(ghxxx)' vec(ghxxu)' vec(ghxuu)' vec(ghuuu)' vec(ghxss)' vec(ghuss)']',
|
||||
% where rowredform_nbr = endo_nbr*(1+nspred+(endo_nbr+1)/2+nspred^2+nspred*exo_nr+exo_nbr^2+1+nspred^3+nspred^2*exo_nbr+nspred*exo_nbr^2+exo_nbr^3+nspred+exo_nbr)
|
||||
% dREDUCEDFORM: [rowredform_nbr by totparam_nbr] in DR order, Jacobian (wrt all params) of REDUCEDFORM
|
||||
% * order==1: corresponds to Iskrev (2010)'s J_2 matrix
|
||||
% * order==2: corresponds to Mutschler (2015)'s J matrix
|
||||
%
|
||||
% DYNAMIC [rowdyn_nbr by 1] in declaration order. Steady state and dynamic model derivatives for all model variables
|
||||
% * order==1: [ys' vec(g1)']', rowdyn_nbr=endo_nbr+length(g1)
|
||||
% * order==2: [ys' vec(g1)' vec(g2)']', rowdyn_nbr=endo_nbr+length(g1)+length(g2)
|
||||
% * order==3: [ys' vec(g1)' vec(g2)' vec(g3)']', rowdyn_nbr=endo_nbr+length(g1)+length(g2)+length(g3)
|
||||
% dDYNAMIC [rowdyn_nbr by modparam_nbr] in declaration order. Jacobian (wrt model parameters) of DYNAMIC
|
||||
% * order==1: corresponds to Ratto and Iskrev (2011)'s J_\Gamma matrix (or LRE)
|
||||
% * order==2: additionally includes derivatives of dynamic hessian
|
||||
%
|
||||
% MOMENTS: [obs_nbr+obs_nbr*(obs_nbr+1)/2+nlags*obs_nbr^2 by 1] in DR order. First two theoretical moments for VAROBS variables, i.e.
|
||||
% [E[yobs]' vech(E[yobs*yobs'])' vec(E[yobs*yobs(-1)'])' ... vec(E[yobs*yobs(-nlag)'])']
|
||||
% [E[varobs]' vech(E[varobs*varobs'])' vec(E[varobs*varobs(-1)'])' ... vec(E[varobs*varobs(-nlag)'])']
|
||||
% dMOMENTS: [obs_nbr+obs_nbr*(obs_nbr+1)/2+nlags*obs_nbr^2 by totparam_nbr] in DR order. Jacobian (wrt all params) of MOMENTS
|
||||
% * order==1: corresponds to Iskrev (2010)'s J matrix
|
||||
% * order==2: corresponds to Mutschler (2015)'s \bar{M}_2 matrix, i.e. theoretical moments from the pruned state space system
|
||||
%
|
||||
% dSPECTRUM: [totparam_nbr by totparam_nbr] in DR order. Gram matrix of Jacobian (wrt all params) of spectral density for VAROBS variables, where
|
||||
% spectral density at frequency w: f(w) = (2*pi)^(-1)*H(exp(-i*w))*E[xi*xi']*ctranspose(H(exp(-i*w)) with H being the Transfer function
|
||||
% spectral density at frequency w: f(w) = (2*pi)^(-1)*H(exp(-i*w))*E[Inov*Inov']*ctranspose(H(exp(-i*w)) with H being the Transfer function
|
||||
% dSPECTRUM = int_{-\pi}^\pi transpose(df(w)/dp')*(df(w)/dp') dw
|
||||
% * order==1: corresponds to Qu and Tkachenko (2012)'s G matrix, where xi and H are computed from linear state space system
|
||||
% * order==2: corresponds to Mutschler (2015)'s G_2 matrix, where xi and H are computed from pruned state space system
|
||||
% * order==1: corresponds to Qu and Tkachenko (2012)'s G matrix, where Inov and H are computed from linear state space system
|
||||
% * order==2: corresponds to Mutschler (2015)'s G_2 matrix, where Inov and H are computed from second-order pruned state space system
|
||||
% * order==3: Inov and H are computed from third-order pruned state space system
|
||||
%
|
||||
% dMINIMAL: [obs_nbr+minx^2+minx*exo_nbr+obs_nbr*minx+obs_nbr*exo_nbr+exo_nbr*(exo_nbr+1)/2 by totparam_nbr+minx^2+exo_nbr^2]
|
||||
% dMINIMAL: [obs_nbr+minx_nbr^2+minx_nbr*exo_nbr+obs_nbr*minx_nbr+obs_nbr*exo_nbr+exo_nbr*(exo_nbr+1)/2 by totparam_nbr+minx_nbr^2+exo_nbr^2]
|
||||
% Jacobian (wrt all params, and similarity_transformation_matrices (T and U)) of observational equivalent minimal ABCD system,
|
||||
% corresponds to Komunjer and Ng (2011)'s Deltabar matrix, where
|
||||
% MINIMAL = [vec(E[yobs]' vec(minA)' vec(minB)' vec(minC)' vec(minD)' vech(Sigma_e)']'
|
||||
% * order==1: E[yobs] is equal to steady state
|
||||
% * order==2: E[yobs] is computed from the pruned state space system (second-order accurate), as noted in section 5 of Komunjer and Ng (2011)
|
||||
% MINIMAL = [vec(E[varobs]' vec(minA)' vec(minB)' vec(minC)' vec(minD)' vech(Sigma_e)']'
|
||||
% minA, minB, minC and minD is the minimal state space system computed in get_minimal_state_representation
|
||||
% * order==1: E[varobs] is equal to steady state
|
||||
% * order==2|3: E[varobs] is computed from the pruned state space system (second|third-order accurate), as noted in section 5 of Komunjer and Ng (2011)
|
||||
%
|
||||
% derivatives_info [structure] for use in dsge_likelihood to compute Hessian analytically.
|
||||
% Contains dA, dB, and d(B*Sigma_e*B'), where A and B are from Kalman filter. Only used at order==1.
|
||||
% derivatives_info [structure] for use in dsge_likelihood to compute Hessian analytically. Only used at order==1.
|
||||
% Contains dA, dB, and d(B*Sigma_e*B'), where A and B are Kalman filter transition matrice.
|
||||
%
|
||||
% -------------------------------------------------------------------------
|
||||
% This function is called by
|
||||
|
|
@ -220,7 +224,7 @@ options.options_ident.indpmodel = indpmodel;
|
|||
options.options_ident.indpstderr = indpstderr;
|
||||
options.options_ident.indpcorr = indpcorr;
|
||||
oo.dr = pruned_state_space_system(M, options, oo.dr);
|
||||
E_y = oo.dr.pruned.E_y; dE_y = oo.dr.pruned.dE_y;
|
||||
MEAN = oo.dr.pruned.E_y; dMEAN = oo.dr.pruned.dE_y;
|
||||
A = oo.dr.pruned.A; dA = oo.dr.pruned.dA;
|
||||
B = oo.dr.pruned.B; dB = oo.dr.pruned.dB;
|
||||
C = oo.dr.pruned.C; dC = oo.dr.pruned.dC;
|
||||
|
|
@ -247,17 +251,17 @@ end
|
|||
% yhat = C*zhat(-1) + D*xi, where yhat = y - E(y)
|
||||
if ~no_identification_moments
|
||||
MOMENTS = identification_numerical_objective(para0, 1, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=1]
|
||||
MOMENTS = [E_y; MOMENTS];
|
||||
MOMENTS = [MEAN; MOMENTS];
|
||||
if kronflag == -1
|
||||
%numerical derivative of autocovariogram
|
||||
dMOMENTS = fjaco(str2func('identification_numerical_objective'), para0, 1, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=1]
|
||||
M.params = params0; %make sure values are set back
|
||||
M.Sigma_e = Sigma_e0; %make sure values are set back
|
||||
M.Correlation_matrix = Corr_e0 ; %make sure values are set back
|
||||
dMOMENTS = [dE_y; dMOMENTS]; %add Jacobian of steady state of VAROBS variables
|
||||
dMOMENTS = [dMEAN; dMOMENTS]; %add Jacobian of steady state of VAROBS variables
|
||||
else
|
||||
dMOMENTS = zeros(obs_nbr + obs_nbr*(obs_nbr+1)/2 + nlags*obs_nbr^2 , totparam_nbr);
|
||||
dMOMENTS(1:obs_nbr,:) = dE_y; %add Jacobian of first moments of VAROBS variables
|
||||
dMOMENTS(1:obs_nbr,:) = dMEAN; %add Jacobian of first moments of VAROBS variables
|
||||
% Denote Ezz0 = E[zhat*zhat'], then the following Lyapunov equation defines the autocovariagram: Ezz0 -A*Ezz0*A' = B*Sig_xi*B' = Om_z
|
||||
[Ezz0,u] = lyapunov_symm(A, Om_z, options.lyapunov_fixed_point_tol, options.qz_criterium, options.lyapunov_complex_threshold, 1, options.debug);
|
||||
stationary_vars = (1:length(indvobs))';
|
||||
|
|
@ -426,7 +430,7 @@ if ~no_identification_spectrum
|
|||
end
|
||||
end
|
||||
% Normalize Matrix and add steady state Jacobian, note that G is real and symmetric by construction
|
||||
dSPECTRUM = 2*pi*dSPECTRUM./(2*length(freqs)-1) + dE_y'*dE_y;
|
||||
dSPECTRUM = 2*pi*dSPECTRUM./(2*length(freqs)-1) + dMEAN'*dMEAN;
|
||||
dSPECTRUM = real(dSPECTRUM);
|
||||
else
|
||||
dSPECTRUM = [];
|
||||
|
|
@ -482,7 +486,7 @@ if ~no_identification_minimal
|
|||
-2*Enu*kron(Sigma_e0,Inu)];
|
||||
dMINIMAL = full([KomunjerNg_DL KomunjerNg_DT KomunjerNg_DU]);
|
||||
%add Jacobian of steady state (here we also allow for higher-order perturbation, i.e. only the mean provides additional restrictions
|
||||
dMINIMAL = [dE_y zeros(obs_nbr,minnx^2+exo_nbr^2); dMINIMAL];
|
||||
dMINIMAL = [dMEAN zeros(obs_nbr,minnx^2+exo_nbr^2); dMINIMAL];
|
||||
end
|
||||
end
|
||||
else
|
||||
|
|
|
|||
|
|
@ -13,22 +13,25 @@ function [out,info] = get_perturbation_params_derivs_numerical_objective(params,
|
|||
% options: [structure] storing the options
|
||||
% -------------------------------------------------------------------------
|
||||
%
|
||||
% OUTPUT (dependent on outputflag and order of approximation):
|
||||
% - 'perturbation_solution': out = [vec(Sigma_e);vec(ghx);vec(ghu);vec(ghxx);vec(ghxu);vec(ghuu);vec(ghs2);vec(ghxxx);vec(ghxxu);vec(ghxuu);vec(ghuuu);vec(ghxss);vec(ghuss)]
|
||||
% - 'dynamic_model': out = [Yss; vec(g1); vec(g2); vec(g3)]
|
||||
% - 'Kalman_Transition': out = [Yss; vec(KalmanA); dyn_vech(KalmanB*Sigma_e*KalmanB')];
|
||||
% all in DR-order
|
||||
% OUTPUT
|
||||
% out (dependent on outputflag and order of approximation):
|
||||
% - 'perturbation_solution': out = out1 = [vec(Sigma_e);vec(ghx);vec(ghu)]; (order==1)
|
||||
% out = out2 = [out1;vec(ghxx);vec(ghxu);vec(ghuu);vec(ghs2)]; (order==2)
|
||||
% out = out3 = [out1;out2;vec(ghxxx);vec(ghxxu);vec(ghxuu);vec(ghuuu);vec(ghxss);vec(ghuss)]; (order==3)
|
||||
% - 'dynamic_model': out = [Yss; vec(g1); vec(g2); vec(g3)]
|
||||
% - 'Kalman_Transition': out = [Yss; vec(KalmanA); dyn_vech(KalmanB*Sigma_e*KalmanB')];
|
||||
% all in DR-order
|
||||
% info [integer] output from resol
|
||||
% -------------------------------------------------------------------------
|
||||
% This function is called by
|
||||
% * get_solution_params_deriv.m (previously getH.m)
|
||||
% * get_identification_jacobians.m (previously getJJ.m)
|
||||
% * get_perturbation_params_derivs.m (previously getH.m)
|
||||
% -------------------------------------------------------------------------
|
||||
% This function calls
|
||||
% * [M.fname,'.dynamic']
|
||||
% * resol
|
||||
% * dyn_vech
|
||||
% =========================================================================
|
||||
% Copyright (C) 2019 Dynare Team
|
||||
% Copyright (C) 2019-2020 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -48,8 +51,8 @@ function [out,info] = get_perturbation_params_derivs_numerical_objective(params,
|
|||
|
||||
%% Update stderr, corr and model parameters and compute perturbation approximation and steady state with updated parameters
|
||||
M = set_all_parameters(params,estim_params,M);
|
||||
Sigma_e = M.Sigma_e;
|
||||
[~,info,M,options,oo] = resol(0,M,options,oo);
|
||||
Sigma_e = M.Sigma_e;
|
||||
|
||||
if info(1) > 0
|
||||
% there are errors in the solution algorithm
|
||||
|
|
@ -60,11 +63,9 @@ else
|
|||
ghx = oo.dr.ghx; ghu = oo.dr.ghu;
|
||||
if options.order > 1
|
||||
ghxx = oo.dr.ghxx; ghxu = oo.dr.ghxu; ghuu = oo.dr.ghuu; ghs2 = oo.dr.ghs2;
|
||||
%ghxs = oo.dr.ghxs; ghus = oo.dr.ghus; %these are zero due to certainty equivalence and Gaussian shocks
|
||||
end
|
||||
if options.order > 2
|
||||
ghxxx = oo.dr.ghxxx; ghxxu = oo.dr.ghxxu; ghxuu = oo.dr.ghxuu; ghxss = oo.dr.ghxss; ghuuu = oo.dr.ghuuu; ghuss = oo.dr.ghuss;
|
||||
%ghxxs = oo.dr.ghxxs; ghxus = oo.dr.ghxus; ghuus = oo.dr.ghuus; ghsss = oo.dr.ghsss; %these are zero due to certainty equivalence and Gaussian shocks
|
||||
end
|
||||
end
|
||||
Yss = ys(oo.dr.order_var); %steady state of model variables in DR order
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
|
|||
% 1: prior exists. Identification is checked for stderr, corr and model parameters as declared in estimated_params block
|
||||
% 0: prior does not exist. Identification is checked for all stderr and model parameters, but no corr parameters
|
||||
% * init [integer]
|
||||
% flag for initialization of persistent vars. This is needed if one may wants to make more calls to identification in the same dynare mod file
|
||||
% flag for initialization of persistent vars. This is needed if one may want to make more calls to identification in the same mod file
|
||||
% -------------------------------------------------------------------------
|
||||
% OUTPUTS
|
||||
% * ide_moments [structure]
|
||||
|
|
@ -36,7 +36,7 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
|
|||
% * ide_spectrum [structure]
|
||||
% identification results using spectrum (Qu and Tkachenko, 2012; Mutschler, 2015)
|
||||
% * ide_minimal [structure]
|
||||
% identification results using minimal system (Komunjer and Ng, 2011)
|
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% identification results using theoretical mean and minimal system (Komunjer and Ng, 2011)
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% * ide_hess [structure]
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% identification results using asymptotic Hessian (Ratto and Iskrev, 2011)
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% * ide_reducedform [structure]
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||||
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|
@ -44,7 +44,7 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
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% * ide_dynamic [structure]
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% identification results using steady state and dynamic model derivatives (Ratto and Iskrev, 2011)
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% * derivatives_info [structure]
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% info about derivatives, used in dsge_likelihood.m
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% info about first-order perturbation derivatives, used in dsge_likelihood.m
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||||
% * info [integer]
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% output from dynare_resolve
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||||
% * options_ident [structure]
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||||
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@ -71,7 +71,7 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
|
|||
% * stoch_simul
|
||||
% * vec
|
||||
% =========================================================================
|
||||
% Copyright (C) 2008-2019 Dynare Team
|
||||
% Copyright (C) 2008-2020 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -91,16 +91,17 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
|
|||
|
||||
global oo_ M_ options_ bayestopt_ estim_params_
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||||
persistent ind_dMOMENTS ind_dREDUCEDFORM ind_dDYNAMIC
|
||||
% persistence is necessary, because in a MC loop the numerical threshold used may provide vectors of different length, leading to crashes in MC loops
|
||||
% persistent indices are necessary, because in a MC loop the numerical threshold
|
||||
% used may provide vectors of different length, leading to crashes in MC loops
|
||||
|
||||
%initialize output structures
|
||||
ide_hess = struct(); %Jacobian of asymptotic/simulated information matrix
|
||||
ide_reducedform = struct(); %Jacobian of steady state and reduced form solution
|
||||
ide_dynamic = struct(); %Jacobian of steady state and dynamic model derivatives
|
||||
ide_moments = struct(); %Jacobian of first two moments (Iskrev, 2010; Mutschler, 2015)
|
||||
ide_spectrum = struct(); %Gram matrix of Jacobian of spectral density plus Gram matrix of Jacobian of steady state (Qu and Tkachenko, 2012; Mutschler, 2015)
|
||||
ide_minimal = struct(); %Jacobian of minimal system (Komunjer and Ng, 2011)
|
||||
derivatives_info = struct(); %storage for Jacobians used in dsge_likelihood.m for (analytical or simulated) asymptotic Gradient and Hession of likelihood
|
||||
ide_hess = struct(); %Identification structure based on asymptotic/simulated information matrix
|
||||
ide_reducedform = struct(); %Identification structure based on steady state and reduced form solution
|
||||
ide_dynamic = struct(); %Identification structure based on steady state and dynamic model derivatives
|
||||
ide_moments = struct(); %Identification structure based on first two moments (Iskrev, 2010; Mutschler, 2015)
|
||||
ide_spectrum = struct(); %Identification structure based on Gram matrix of Jacobian of spectral density plus Gram matrix of Jacobian of steady state (Qu and Tkachenko, 2012; Mutschler, 2015)
|
||||
ide_minimal = struct(); %Identification structure based on mean and minimal system (Komunjer and Ng, 2011)
|
||||
derivatives_info = struct(); %storage for first-order perturbation Jacobians used in dsge_likelihood.m
|
||||
|
||||
totparam_nbr = length(params); %number of all parameters to be checked
|
||||
modparam_nbr = length(indpmodel); %number of model parameters to be checked
|
||||
|
|
@ -114,11 +115,6 @@ if ~isempty(estim_params_)
|
|||
end
|
||||
|
||||
%get options (see dynare_identification.m for description of options)
|
||||
no_identification_strength = options_ident.no_identification_strength;
|
||||
no_identification_reducedform = options_ident.no_identification_reducedform;
|
||||
no_identification_moments = options_ident.no_identification_moments;
|
||||
no_identification_minimal = options_ident.no_identification_minimal;
|
||||
no_identification_spectrum = options_ident.no_identification_spectrum;
|
||||
order = options_ident.order;
|
||||
nlags = options_ident.ar;
|
||||
advanced = options_ident.advanced;
|
||||
|
|
@ -130,11 +126,17 @@ checks_via_subsets = options_ident.checks_via_subsets;
|
|||
tol_deriv = options_ident.tol_deriv;
|
||||
tol_rank = options_ident.tol_rank;
|
||||
tol_sv = options_ident.tol_sv;
|
||||
no_identification_strength = options_ident.no_identification_strength;
|
||||
no_identification_reducedform = options_ident.no_identification_reducedform;
|
||||
no_identification_moments = options_ident.no_identification_moments;
|
||||
no_identification_minimal = options_ident.no_identification_minimal;
|
||||
no_identification_spectrum = options_ident.no_identification_spectrum;
|
||||
|
||||
%Compute linear approximation and fill dr structure
|
||||
[oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
|
||||
|
||||
if info(1) == 0 %no errors in solution
|
||||
% Compute parameter Jacobians for identification analysis
|
||||
[MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params_, M_, oo_, options_, options_ident, indpmodel, indpstderr, indpcorr, indvobs);
|
||||
if isempty(dMINIMAL)
|
||||
% Komunjer and Ng is not computed if (1) minimality conditions are not fullfilled or (2) there are more shocks and measurement errors than observables, so we need to reset options
|
||||
|
|
|
|||
Loading…
Reference in New Issue