function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = run(M_,oo_,options_,bayestopt_,estim_params_,options_ident, pdraws0) % [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = run(options_ident, pdraws0) % ------------------------------------------------------------------------- % This function is called, when the user specifies identification(...); in the mod file. It prepares all identification analysis: % (1) set options, local and persistent variables for a new identification % analysis either for a single point or a MC Sample. It also displays and plots the results % (2) load, display and plot a previously saved identification analysis % % Note 1: This function does not output the arguments to the workspace, but saves results to the folder identification % Note 2: If you want to use this function directly in the mod file and workspace, you still have % to put identification in your mod file, otherwise the preprocessor won't provide all necessary objects % ========================================================================= % INPUTS % * M_ [structure] Matlab's structure describing the model % * oo_ [structure] Matlab's structure describing the results % * options_ [structure] Matlab's structure describing the current options % * bayestopt_ [structure] describing the priors % * estim_params_ [structure] characterizing parameters to be estimated % * options_ident [structure] identification options % * pdraws0 [SampleSize by totparam_nbr] optional: matrix of MC sample of model parameters % ------------------------------------------------------------------------- % OUTPUTS % * pdraws [matrix] MC sample of model params used % * STO_REDUCEDFORM, [matrix] MC sample of entries in steady state and reduced form model solution (stacked vertically) % * STO_MOMENTS, [matrix] MC sample of entries in theoretical first two moments (stacked vertically) % * STO_DYNAMIC, [matrix] MC sample of entries in steady state and dynamic model derivatives (stacked vertically) % * STO_si_dDYNAMIC, [matrix] MC sample of derivatives of steady state and dynamic derivatives % * STO_si_dREDUCEDFORM, [matrix] MC sample of derivatives of steady state and reduced form model solution % * STO_si_dMOMENTS [matrix] MC sample of Iskrev (2010)'s J matrix % * STO_dSPECTRUM [matrix] MC sample of Qu and Tkachenko (2012)'s \bar{G} matrix % * STO_dMINIMAL [matrix] MC sample of Komunjer and Ng (2011)'s \bar{\Delta} matrix % ------------------------------------------------------------------------- % This function is called by % * driver.m % * gsa.map_identification.m % ------------------------------------------------------------------------- % This function calls % * checkpath % * identification.display % * dyn_waitbar % * dyn_waitbar_close % * get_all_parameters % * get_posterior_parameters % * get_the_name % * identification.analysis % * isoctave % * identification.plot % * dprior.draw % * set_default_option % * set_prior % * skipline % * vnorm % ========================================================================= % Copyright © 2010-2023 Dynare Team % % This file is part of Dynare. % % Dynare is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % Dynare is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with Dynare. If not, see . % ========================================================================= fname = M_.fname; %model name dname = M_.dname; %model name %turn warnings off, either globally or only relevant ids if isoctave %warning('off'), warning('off','Octave:singular-matrix'); warning('off','Octave:nearly-singular-matrix'); warning('off','Octave:neg-dim-as-zero'); warning('off','Octave:array-as-logical'); else %warning off; warning('off','MATLAB:rankDeficientMatrix'); warning('off','MATLAB:singularMatrix'); warning('off','MATLAB:nearlySingularMatrix'); warning('off','MATLAB:plot:IgnoreImaginaryXYPart'); warning('off','MATLAB:specgraph:private:specgraph:UsingOnlyRealComponentOfComplexData'); warning('off','MATLAB:handle_graphics:exceptions:SceneNode'); warning('off','MATLAB:log:logOfZero'); end %% Set all options in options_ident and create objects options_ident = set_default_option(options_ident,'gsa_sample_file',0); % 0: do not use sample file % 1: triggers gsa prior sample % 2: triggers gsa Monte-Carlo sample (i.e. loads a sample corresponding to pprior=0 and ppost=0 in sensitivity.run options) % FILENAME: use sample file in provided path options_ident = set_default_option(options_ident,'parameter_set','prior_mean'); % 'calibration': use values in M_.params and M_.Sigma_e to update estimated stderr, corr and model parameters (get_all_parameters) % 'prior_mode': use values in bayestopt_.p5 prior to update estimated stderr, corr and model parameters % 'prior_mean': use values in bayestopt_.p1 prior to update estimated stderr, corr and model parameters % 'posterior_mode': use posterior mode values in estim_params_ to update estimated stderr, corr and model parameters (get_posterior_parameters('mode',...)) % 'posterior_mean': use posterior mean values in estim_params_ to update estimated stderr, corr and model parameters (get_posterior_parameters('mean',...)) % 'posterior_median': use posterior median values in estim_params_ to update estimated stderr, corr and model parameters (get_posterior_parameters('median',...)) options_ident = set_default_option(options_ident,'load_ident_files',0); % 1: load previously computed analysis from identification/fname_identif.mat options_ident = set_default_option(options_ident,'useautocorr',0); % 1: use autocorrelations in Iskrev (2010)'s theoretical second moments criteria % 0: use autocovariances in Iskrev (2010)'s theoretical second moments criteria options_ident = set_default_option(options_ident,'ar',1); % number of lags to consider for autocovariances/autocorrelations in Iskrev (2010)'s criteria options_ident = set_default_option(options_ident,'prior_mc',1); % size of Monte-Carlo sample of parameter draws options_ident = set_default_option(options_ident,'prior_range',0); % 1: sample uniformly from prior ranges implied by the prior specifications (overwrites prior shape when prior_mc > 1) % 0: sample from specified prior distributions (when prior_mc > 1) options_ident = set_default_option(options_ident,'periods',300); % length of stochastic simulation to compute simulated moment uncertainty (when analytic Hessian is not available) options_ident = set_default_option(options_ident,'replic',100); % number of replicas to compute simulated moment uncertainty (when analytic Hessian is not available) options_ident = set_default_option(options_ident,'advanced',0); % 1: show a more detailed analysis based on reduced-form model solution and dynamic model derivatives. Further, performs a brute force % search of the groups of parameters best reproducing the behavior of each single parameter. options_ident = set_default_option(options_ident,'normalize_jacobians',1); % 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 options_ident = set_default_option(options_ident,'grid_nbr',5000); % number of grid points in [-pi;pi] to approximate the integral to compute Qu and Tkachenko (2012)'s criteria % 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 if mod(options_ident.grid_nbr,2) ~= 0 options_ident.grid_nbr = options_ident.grid_nbr+1; fprintf('IDENTIFICATION: ''grid_nbr'' needs to be even, hence it is reset to %d\n',options_ident.grid_nbr) if mod(options_ident.grid_nbr,2) ~= 0 error('IDENTIFICATION: You need to set an even value for ''grid_nbr'''); end end options_ident = set_default_option(options_ident,'tol_rank','robust'); % tolerance level used for rank computations options_ident = set_default_option(options_ident,'tol_deriv',1.e-8); % tolerance level for selecting columns of non-zero derivatives options_ident = set_default_option(options_ident,'tol_sv',1.e-3); % tolerance level for selecting non-zero singular values in identification.checks.m options_ident = set_default_option(options_ident,'schur_vec_tol',1e-11); % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix. %check whether to compute identification strength based on information matrix if ~isfield(options_ident,'no_identification_strength') options_ident.no_identification_strength = 0; else options_ident.no_identification_strength = 1; end %check whether to compute and display identification criteria based on steady state and reduced-form model solution if ~isfield(options_ident,'no_identification_reducedform') options_ident.no_identification_reducedform = 0; else options_ident.no_identification_reducedform = 1; end %check whether to compute and display identification criteria based on Iskrev (2010), i.e. derivative of first two moments if ~isfield(options_ident,'no_identification_moments') options_ident.no_identification_moments = 0; else options_ident.no_identification_moments = 1; end %check whether to compute and display identification criteria based on Komunjer and Ng (2011), i.e. derivative of first moment, minimal state space system and observational equivalent spectral density transformation if ~isfield(options_ident,'no_identification_minimal') options_ident.no_identification_minimal = 0; else options_ident.no_identification_minimal = 1; end %Check whether to compute and display identification criteria based on Qu and Tkachenko (2012), i.e. Gram matrix of derivatives of first moment plus outer product of derivatives of spectral density if ~isfield(options_ident,'no_identification_spectrum') options_ident.no_identification_spectrum = 0; else options_ident.no_identification_spectrum = 1; end %overwrite setting, as identification strength and advanced need both reducedform and moments criteria if (isfield(options_ident,'no_identification_strength') && options_ident.no_identification_strength == 0) || (options_ident.advanced == 1) options_ident.no_identification_reducedform = 0; options_ident.no_identification_moments = 0; end %overwrite setting, as sensitivity.run does not make use of spectrum and minimal system if isfield(options_,'opt_gsa') && isfield(options_.opt_gsa,'identification') && options_.opt_gsa.identification == 1 options_ident.no_identification_minimal = 1; options_ident.no_identification_spectrum = 1; end %Deal with non-stationary cases if isfield(options_ident,'diffuse_filter') options_ident.lik_init=3; %overwrites any other lik_init set options_.diffuse_filter=options_ident.diffuse_filter; %make options_ inherit diffuse filter; will overwrite any conflicting lik_init in dynare_estimation_init else if options_.diffuse_filter==1 %warning if estimation with diffuse filter was done, but not passed fprintf('WARNING IDENTIFICATION: Previously the diffuse_filter option was used, but it was not passed to the identification command. This may result in problems if your model contains unit roots.\n'); end end options_ident = set_default_option(options_ident,'lik_init',1); options_.lik_init=options_ident.lik_init; %make options_ inherit lik_init if options_ident.lik_init==3 %user specified diffuse filter using the lik_init option options_ident.analytic_derivation=0; %diffuse filter not compatible with analytic derivation end % Type of initialization of Kalman filter: % 1: stationary models: initial matrix of variance of error of forecast is set equal to the unconditional variance of the state variables % 2: nonstationary models: wide prior is used with an initial matrix of variance of the error of forecast diagonal with 10 on the diagonal (follows the suggestion of Harvey and Phillips(1979)) % 3: nonstationary models: use a diffuse filter (use rather the diffuse_filter option) % 4: filter is initialized with the fixed point of the Riccati equation % 5: i) option 2 for non-stationary elements by setting their initial variance in the forecast error matrix to 10 on the diagonal and all co-variances to 0 and % ii) option 1 for the stationary elements options_ident = set_default_option(options_ident,'analytic_derivation',1); % 1: analytic derivation of gradient and hessian of likelihood in dsge_likelihood.m, only works for stationary models, i.e. kalman_algo<3 options_ident = set_default_option(options_ident,'order',1); % 1: first-order perturbation approximation, identification is based on linear state space system % 2: second-order perturbation approximation, identification is based on second-order pruned state space system % 3: third-order perturbation approximation, identification is based on third-order pruned state space system %overwrite values in options_, note that options_ is restored at the end of the function if isfield(options_ident,'prior_trunc') options_.prior_trunc=options_ident.prior_trunc; % sets truncation of prior end if isfield(options_ident,'TeX') options_.TeX=options_ident.TeX; % requests printing of results and graphs in LaTeX end if isfield(options_ident,'nograph') options_.nograph=options_ident.nograph; % do not display and do not save graphs end if isfield(options_ident,'nodisplay') options_.nodisplay=options_ident.nodisplay; % do not display, but save graphs end if isfield(options_ident,'graph_format') options_.graph_format=options_ident.graph_format; % specify file formats to save graphs: eps, pdf, fig, none (do not save but only display) end % check for external draws, i.e. set pdraws0 for a gsa analysis if options_ident.gsa_sample_file GSAFolder = checkpath('gsa',dname); if options_ident.gsa_sample_file==1 load([GSAFolder,filesep,fname,'_prior'],'lpmat','lpmat0','istable'); elseif options_ident.gsa_sample_file==2 load([GSAFolder,filesep,fname,'_mc'],'lpmat','lpmat0','istable'); else load([GSAFolder,filesep,options_ident.gsa_sample_file],'lpmat','lpmat0','istable'); end if isempty(istable) istable=1:size(lpmat,1); end if ~isempty(lpmat0) lpmatx=lpmat0(istable,:); else lpmatx=[]; end pdraws0 = [lpmatx lpmat(istable,:)]; clear lpmat lpmat0 istable; elseif nargin==6 pdraws0=[]; end external_sample=0; if nargin==7 || ~isempty(pdraws0) % change settings if there is an external sample provided as input argument options_ident.prior_mc = size(pdraws0,1); options_ident.load_ident_files = 0; external_sample = 1; end % Check if estimated_params block is provided if isempty(estim_params_) prior_exist = 0; %reset some options options_ident.prior_mc = 1; options_ident.prior_range = 0; options_.identification_check_endogenous_params_with_no_prior = true; %needed to trigger endogenous steady state parameter check in dynare_estimation_init else prior_exist = 1; parameters = options_ident.parameter_set; end % overwrite settings in options_ and prepare to call dynare_estimation_init options_.order = options_ident.order; if options_ident.order > 1 %order>1 is not compatible with analytic derivation in dsge_likelihood.m options_ident.analytic_derivation=0; %order>1 is based on pruned state space system options_.pruning = true; end if options_ident.order == 3 options_.k_order_solver = 1; end options_.ar = options_ident.ar; options_.prior_mc = options_ident.prior_mc; options_.schur_vec_tol = options_ident.schur_vec_tol; options_.nomoments = 0; options_.analytic_derivation=options_ident.analytic_derivation; % 1: analytic derivation of gradient and hessian of likelihood in dsge_likelihood.m, only works for stationary models, i.e. kalman_algo<3 options_ = set_default_option(options_,'datafile',''); options_.mode_compute = 0; options_.plot_priors = 0; options_.smoother = 1; options_.options_ident = []; [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_); % set method to compute identification Jacobians (kronflag). Default:0 options_ident = set_default_option(options_ident,'analytic_derivation_mode', options_.analytic_derivation_mode); % if not set by user, inherit default global one % 0: efficient sylvester equation method to compute analytical derivatives as in Ratto & Iskrev (2012) % 1: kronecker products method to compute analytical derivatives as in Iskrev (2010) (only for order=1) % -1: numerical two-sided finite difference method to compute numerical derivatives of all identification Jacobians using function identification.numerical_objective.m (previously thet2tau.m) % -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 if options_.discretionary_policy || options_.ramsey_policy if options_ident.analytic_derivation_mode~=-1 fprintf('identification.run: discretionary_policy and ramsey_policy require analytic_derivation_mode=-1. Resetting the option.') options_ident.analytic_derivation_mode=-1; end end options_.analytic_derivation_mode = options_ident.analytic_derivation_mode; %overwrite setting in options_ % Instantiate dprior object (Prior) if prior_exist if any(bayestopt_.pshape > 0) if options_ident.prior_range %sample uniformly from prior ranges (overwrite prior specification) Prior = dprior(bayestopt_, options_.prior_trunc, true); else %sample from prior distributions Prior = dprior(bayestopt_, options_.prior_trunc, false); end else options_ident.prior_mc = 1; %only one single point end end % check if identification directory is already created IdentifDirectoryName = CheckPath('identification',dname); % Create indices for stderr, corr and model parameters if prior_exist % use estimated_params block indpmodel = []; %initialize index for model parameters if ~isempty(estim_params_.param_vals) indpmodel = estim_params_.param_vals(:,1); %values correspond to parameters declaration order, row number corresponds to order in estimated_params end indpstderr=[]; %initialize index for stderr parameters if ~isempty(estim_params_.var_exo) indpstderr = estim_params_.var_exo(:,1); %values correspond to varexo declaration order, row number corresponds to order in estimated_params end indpcorr=[]; %initialize matrix for corr paramters if ~isempty(estim_params_.corrx) indpcorr = estim_params_.corrx(:,1:2); %values correspond to varexo declaration order, row number corresponds to order in estimated_params end totparam_nbr = length(bayestopt_.name); % number of estimated stderr, corr and model parameters as declared in estimated_params modparam_nbr = estim_params_.np; % number of model parameters as declared in estimated_params stderrparam_nbr = estim_params_.nvx; % number of stderr parameters corrparam_nbr = estim_params_.ncx; % number of corr parameters if estim_params_.nvn || estim_params_.ncn %nvn is number of stderr parameters and ncn is number of corr parameters of measurement innovations as declared in estimated_params error('Identification does not (yet) support measurement errors. Instead, define them explicitly as varexo and provide measurement equations in the model definition.') end name = cell(totparam_nbr,1); %initialize cell for parameter names name_tex = cell(totparam_nbr,1); %initialize cell for TeX parameter names for jj=1:totparam_nbr if options_.TeX [param_name_temp, param_name_tex_temp]= get_the_name(jj,options_.TeX,M_,estim_params_,options_.varobs); name_tex{jj,1} =param_name_tex_temp; name{jj,1} = param_name_temp; %ordering corresponds to estimated_params else param_name_temp = get_the_name(jj,options_.TeX,M_,estim_params_,options_.varobs); name{jj,1} = param_name_temp; %ordering corresponds to estimated_params end end else % no estimated_params block, choose all model parameters and all stderr parameters, but no corr parameters indpmodel = 1:M_.param_nbr; %all model parameters indpstderr = 1:M_.exo_nbr; %all stderr parameters indpcorr = []; %no corr parameters stderrparam_nbr = M_.exo_nbr; corrparam_nbr = 0; modparam_nbr = M_.param_nbr; totparam_nbr = modparam_nbr+stderrparam_nbr; name = cellfun(@(x) horzcat('SE_', x), M_.exo_names, 'UniformOutput', false); %names for stderr parameters name = vertcat(name, M_.param_names); name_tex = cellfun(@(x) horzcat('$ SE_{', x, '} $'), M_.exo_names_tex, 'UniformOutput', false); name_tex = vertcat(name_tex, cellfun(@(x) horzcat('$ ', x, ' $'), M_.param_names_tex, 'UniformOutput', false)); if ~isequal(M_.H,0) fprintf('\nidentification.run:: 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') end end options_ident.name_tex = name_tex; fprintf('\n======== Identification Analysis ========\n') if options_ident.order > 1 fprintf('Based on Pruned State Space System (order=%d)\n',options_ident.order); end skipline() if totparam_nbr < 2 options_ident.advanced = 0; fprintf('There is only one parameter to study for identitification. The advanced option is re-set to 0.\n') end % settings dependent on number of parameters options_ident = set_default_option(options_ident,'max_dim_cova_group',min([2,totparam_nbr-1])); options_ident.max_dim_cova_group = min([options_ident.max_dim_cova_group,totparam_nbr-1]); % In brute force search (identification.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 options_ident = set_default_option(options_ident,'checks_via_subsets',0); % 1: uses identification.checks_via_subsets.m to compute problematic parameter combinations % 0: uses identification.checks.m to compute problematic parameter combinations [default] options_ident = set_default_option(options_ident,'max_dim_subsets_groups',min([4,totparam_nbr-1])); % 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 % store identification options options_.options_ident = options_ident; store_options_ident = options_ident; % get some options for quick reference iload = options_ident.load_ident_files; SampleSize = options_ident.prior_mc; if iload <=0 %% Perform new identification analysis, i.e. do not load previous analysis if prior_exist % use information from estimated_params block params = set_prior(estim_params_,M_,options_)'; if all(bayestopt_.pshape == 0) % only bounds are specified in estimated_params parameters = 'ML_Starting_value'; parameters_TeX = 'ML starting value'; fprintf('Testing ML Starting value\n'); else % use user-defined option switch parameters case 'calibration' parameters_TeX = 'Calibration'; fprintf('Testing calibration\n'); params(1,:) = get_all_parameters(estim_params_,M_); case 'posterior_mode' parameters_TeX = 'Posterior mode'; fprintf('Testing posterior mode\n'); params(1,:) = get_posterior_parameters('mode',M_,estim_params_,oo_,options_); case 'posterior_mean' parameters_TeX = 'Posterior mean'; fprintf('Testing posterior mean\n'); params(1,:) = get_posterior_parameters('mean',M_,estim_params_,oo_,options_); case 'posterior_median' parameters_TeX = 'Posterior median'; fprintf('Testing posterior median\n'); params(1,:) = get_posterior_parameters('median',M_,estim_params_,oo_,options_); case 'prior_mode' parameters_TeX = 'Prior mode'; fprintf('Testing prior mode\n'); params(1,:) = bayestopt_.p5(:); case 'prior_mean' parameters_TeX = 'Prior mean'; fprintf('Testing prior mean\n'); params(1,:) = bayestopt_.p1; otherwise fprintf('The option parameter_set has to be equal to: ''calibration'', ''posterior_mode'', ''posterior_mean'', ''posterior_median'', ''prior_mode'' or ''prior_mean''.\n'); error('IDENTIFICATION: The option ''parameter_set'' has an invalid value'); end end else % no estimated_params block is available, all stderr and model parameters, but no corr parameters are chosen params = [sqrt(diag(M_.Sigma_e))', M_.params']; parameters = 'Current_params'; parameters_TeX = 'Current parameter values'; fprintf('Testing all current stderr and model parameter values\n'); end options_ident.tittxt = parameters; %title text for graphs and figures % perform identification analysis for single point [ide_moments_point, ide_spectrum_point, ide_minimal_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, derivatives_info_point, info, error_indicator_point] = ... identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end implies initialization of persistent variables if info(1)~=0 % there are errors in the solution algorithm message = get_error_message(info,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 fprintf('Try sampling up to 50 parameter sets from the prior.\n'); kk=0; while kk<50 && info(1) kk=kk+1; params = Prior.draw(); 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, error_indicator_point] = ... identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); end end if info(1) fprintf('\n-----------\n'); fprintf('Identification stopped:\n'); if any(bayestopt_.pshape) fprintf('The model did not solve for any of 50 attempts of random samples from the prior\n'); end fprintf('-----------\n'); return else % found a (random) point that solves the model 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'); % display results of identification analysis identification.display(params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident); if ~options_ident.no_identification_strength && ~options_.nograph && ~error_indicator_point.identification_strength && ~error_indicator_point.identification_moments % plot (i) identification strength and sensitivity measure based on the moment information matrix and (ii) plot advanced analysis graphs identification.plot(M_,params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, ... IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, parameters_TeX, name_tex); end if SampleSize > 1 % initializations for Monte Carlo Analysis 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 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.m may crash end while iteration < SampleSize if external_sample params = pdraws0(iteration+1,:); % loaded draws else params = Prior.draw(); % new random draw from prior end options_ident.tittxt = []; % clear title text for graphs and figures % run identification analysis [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide_dynamic, ide_derivatives_info, info, error_indicator] = ... identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_MC, dataset_info, prior_exist, 0); % the 0 implies that we do not initialize persistent variables anymore 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(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 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.ino = 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.ino = 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 STO_si_dREDUCEDFORM = {}; STO_REDUCEDFORM = {}; IDE_REDUCEDFORM = {}; end % preallocate storage for moments if ~options_MC.no_identification_moments && ~isempty(fieldnames(ide_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); IDE_MOMENTS.ind_dMOMENTS = ide_moments.ind_dMOMENTS; IDE_MOMENTS.ino = 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 STO_si_dMOMENTS = {}; STO_MOMENTS = {}; IDE_MOMENTS = {}; end % preallocate storage for spectrum if ~options_MC.no_identification_spectrum && ~isempty(fieldnames(ide_spectrum)) 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.ino = 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 STO_dSPECTRUM = {}; IDE_SPECTRUM = {}; end % preallocate storage for minimal system if ~options_MC.no_identification_minimal && ~isempty(fieldnames(ide_minimal)) && ide_minimal.minimal_state_space==1 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.ino = 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.minimal_state_space = zeros(SampleSize, 1); else STO_dMINIMAL = {}; IDE_MINIMAL = {}; end end if info(1)==0 % if admissable draw iteration = iteration + 1; %increase total index of admissable draws run_index = run_index + 1; %increase index of admissable draws after saving to files pdraws(iteration,:) = params; % store draw % store results for steady state and dynamic model derivatives STO_DYNAMIC(:,iteration) = ide_dynamic.DYNAMIC; STO_si_dDYNAMIC(:,:,run_index) = ide_dynamic.si_dDYNAMIC; IDE_DYNAMIC.cond(iteration,1) = ide_dynamic.cond; IDE_DYNAMIC.ino(iteration,1) = ide_dynamic.ino; IDE_DYNAMIC.ind0(iteration,:) = ide_dynamic.ind0; IDE_DYNAMIC.jweak(iteration,:) = ide_dynamic.jweak; IDE_DYNAMIC.jweak_pair(iteration,:) = ide_dynamic.jweak_pair; IDE_DYNAMIC.Mco(iteration,:) = ide_dynamic.Mco; % store results for reduced form if ~options_MC.no_identification_reducedform && ~error_indicator.identification_reducedform 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; IDE_REDUCEDFORM.jweak(iteration,:) = ide_reducedform.jweak; IDE_REDUCEDFORM.jweak_pair(iteration,:) = ide_reducedform.jweak_pair; IDE_REDUCEDFORM.Mco(iteration,:) = ide_reducedform.Mco; end % store results for moments if ~options_MC.no_identification_moments && ~error_indicator.identification_moments STO_MOMENTS(:,iteration) = ide_moments.MOMENTS; STO_si_dMOMENTS(:,:,run_index) = ide_moments.si_dMOMENTS; IDE_MOMENTS.cond(iteration,1) = ide_moments.cond; IDE_MOMENTS.ino(iteration,1) = ide_moments.ino; IDE_MOMENTS.ind0(iteration,:) = ide_moments.ind0; IDE_MOMENTS.jweak(iteration,:) = ide_moments.jweak; IDE_MOMENTS.jweak_pair(iteration,:) = ide_moments.jweak_pair; IDE_MOMENTS.Mco(iteration,:) = ide_moments.Mco; IDE_MOMENTS.S(iteration,:) = ide_moments.S; IDE_MOMENTS.V(iteration,:,:) = ide_moments.V; end % store results for spectrum if ~options_MC.no_identification_spectrum && ~error_indicator.identification_spectrum STO_dSPECTRUM(:,:,run_index) = ide_spectrum.dSPECTRUM; IDE_SPECTRUM.cond(iteration,1) = ide_spectrum.cond; IDE_SPECTRUM.ino(iteration,1) = ide_spectrum.ino; IDE_SPECTRUM.ind0(iteration,:) = ide_spectrum.ind0; IDE_SPECTRUM.jweak(iteration,:) = ide_spectrum.jweak; IDE_SPECTRUM.jweak_pair(iteration,:) = ide_spectrum.jweak_pair; IDE_SPECTRUM.Mco(iteration,:) = ide_spectrum.Mco; end % store results for minimal system if ~options_MC.no_identification_minimal if ~error_indicator.identification_minimal STO_dMINIMAL(:,:,run_index) = ide_minimal.dMINIMAL; IDE_MINIMAL.cond(iteration,1) = ide_minimal.cond; IDE_MINIMAL.ino(iteration,1) = ide_minimal.ino; IDE_MINIMAL.ind0(iteration,:) = ide_minimal.ind0; IDE_MINIMAL.jweak(iteration,:) = ide_minimal.jweak; IDE_MINIMAL.jweak_pair(iteration,:) = ide_minimal.jweak_pair; IDE_MINIMAL.Mco(iteration,:) = ide_minimal.Mco; IDE_MINIMAL.minimal_state_space(iteration,:) = ide_minimal.minimal_state_space; else IDE_MINIMAL.minimal_state_space(iteration,:) = ide_minimal.minimal_state_space; end end % 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 %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 STO_si_dREDUCEDFORM = STO_si_dREDUCEDFORM(:,:,1:run_index); end if ~options_MC.no_identification_moments STO_si_dMOMENTS = STO_si_dMOMENTS(:,:,1:run_index); end if ~options_MC.no_identification_spectrum STO_dSPECTRUM = STO_dSPECTRUM(:,:,1:run_index); end if ~options_MC.no_identification_minimal STO_dMINIMAL = STO_dMINIMAL(:,:,1:run_index); end end save([IdentifDirectoryName '/' fname '_identif_' int2str(file_index) '.mat'], 'STO_si_dDYNAMIC'); STO_si_dDYNAMIC = zeros(size(STO_si_dDYNAMIC)); % reset storage if ~options_MC.no_identification_reducedform save([IdentifDirectoryName '/' fname '_identif_' int2str(file_index) '.mat'], 'STO_si_dREDUCEDFORM', '-append'); STO_si_dREDUCEDFORM = zeros(size(STO_si_dREDUCEDFORM)); % reset storage end if ~options_MC.no_identification_moments save([IdentifDirectoryName '/' fname '_identif_' int2str(file_index) '.mat'], 'STO_si_dMOMENTS','-append'); STO_si_dMOMENTS = zeros(size(STO_si_dMOMENTS)); % reset storage end if ~options_MC.no_identification_spectrum save([IdentifDirectoryName '/' fname '_identif_' int2str(file_index) '.mat'], 'STO_dSPECTRUM','-append'); STO_dSPECTRUM = zeros(size(STO_dSPECTRUM)); % reset storage end if ~options_MC.no_identification_minimal save([IdentifDirectoryName '/' fname '_identif_' int2str(file_index) '.mat'], 'STO_dMINIMAL','-append'); STO_dMINIMAL = zeros(size(STO_dMINIMAL)); % reset storage end run_index = 0; % reset index end if SampleSize > 1 dyn_waitbar(iteration/SampleSize, h, ['MC identification checks ', int2str(iteration), '/', int2str(SampleSize)]); end end end if SampleSize > 1 dyn_waitbar_close(h); normalize_STO_DYNAMIC = std(STO_DYNAMIC,0,2); if ~options_MC.no_identification_reducedform normalize_STO_REDUCEDFORM = std(STO_REDUCEDFORM,0,2); end if ~options_MC.no_identification_moments normalize_STO_MOMENTS = std(STO_MOMENTS,0,2); end if ~options_MC.no_identification_minimal normalize_STO_MINIMAL = 1; %not used (yet) end if ~options_MC.no_identification_spectrum normalize_STO_SPECTRUM = 1; %not used (yet) end normaliz1 = std(pdraws); iter = 0; for ifile_index = 1:file_index load([IdentifDirectoryName '/' fname '_identif_' int2str(ifile_index) '.mat'], 'STO_si_dDYNAMIC'); maxrun_dDYNAMIC = size(STO_si_dDYNAMIC,3); if ~options_MC.no_identification_reducedform load([IdentifDirectoryName '/' fname '_identif_' int2str(ifile_index) '.mat'], 'STO_si_dREDUCEDFORM'); maxrun_dREDUCEDFORM = size(STO_si_dREDUCEDFORM,3); else maxrun_dREDUCEDFORM = 0; end if ~options_MC.no_identification_moments load([IdentifDirectoryName '/' fname '_identif_' int2str(ifile_index) '.mat'], 'STO_si_dMOMENTS'); maxrun_dMOMENTS = size(STO_si_dMOMENTS,3); else maxrun_dMOMENTS = 0; end if ~options_MC.no_identification_spectrum load([IdentifDirectoryName '/' fname '_identif_' int2str(ifile_index) '.mat'], 'STO_dSPECTRUM'); maxrun_dSPECTRUM = size(STO_dSPECTRUM,3); else maxrun_dSPECTRUM = 0; end if ~options_MC.no_identification_minimal load([IdentifDirectoryName '/' fname '_identif_' int2str(ifile_index) '.mat'], 'STO_dMINIMAL'); maxrun_dMINIMAL = size(STO_dMINIMAL,3); else maxrun_dMINIMAL = 0; end si_dDYNAMICnorm=NaN(max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL]),size(STO_si_dDYNAMIC,2)); if ~options_MC.no_identification_reducedform si_dREDUCEDFORMnorm=NaN(max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL]),size(STO_si_dREDUCEDFORM,2)); end if ~options_MC.no_identification_moments si_dMOMENTSnorm=NaN(max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL]),size(STO_si_dMOMENTS,2)); end if ~options_MC.no_identification_spectrum dSPECTRUMnorm=NaN(max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL]),size(STO_dSPECTRUM,2)); end if ~options_MC.no_identification_minimal dMINIMALnorm=NaN(max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL]),size(STO_dMINIMAL,2)); end for irun=1:max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL]) iter=iter+1; % note that this is not the same si_dDYNAMICnorm as computed in identification.analysis % given that we have the MC sample of the Jacobians, we also normalize by the std of the sample of Jacobian entries, to get a fully standardized sensitivity measure si_dDYNAMICnorm(iter,:) = identification.vnorm(STO_si_dDYNAMIC(:,:,irun)./repmat(normalize_STO_DYNAMIC,1,totparam_nbr-(stderrparam_nbr+corrparam_nbr))).*normaliz1((stderrparam_nbr+corrparam_nbr)+1:end); if ~options_MC.no_identification_reducedform && ~isempty(STO_si_dREDUCEDFORM) % note that this is not the same si_dREDUCEDFORMnorm as computed in identification.analysis % given that we have the MC sample of the Jacobians, we also normalize by the std of the sample of Jacobian entries, to get a fully standardized sensitivity measure si_dREDUCEDFORMnorm(iter,:) = identification.vnorm(STO_si_dREDUCEDFORM(:,:,irun)./repmat(normalize_STO_REDUCEDFORM,1,totparam_nbr)).*normaliz1; end if ~options_MC.no_identification_moments && ~isempty(STO_si_dMOMENTS) % note that this is not the same si_dMOMENTSnorm as computed in identification.analysis % given that we have the MC sample of the Jacobians, we also normalize by the std of the sample of Jacobian entries, to get a fully standardized sensitivity measure si_dMOMENTSnorm(iter,:) = identification.vnorm(STO_si_dMOMENTS(:,:,irun)./repmat(normalize_STO_MOMENTS,1,totparam_nbr)).*normaliz1; end if ~options_MC.no_identification_spectrum && ~isempty(STO_dSPECTRUM) % note that this is not the same dSPECTRUMnorm as computed in identification.analysis dSPECTRUMnorm(iter,:) = identification.vnorm(STO_dSPECTRUM(:,:,irun)); %not yet used end if ~options_MC.no_identification_minimal && ~isempty(STO_dMINIMAL) % note that this is not the same dMINIMALnorm as computed in identification.analysis dMINIMALnorm(iter,:) = identification.vnorm(STO_dMINIMAL(:,:,irun)); %not yet used end end end IDE_DYNAMIC.si_dDYNAMICnorm = si_dDYNAMICnorm; save([IdentifDirectoryName '/' fname '_identif.mat'], 'pdraws', 'IDE_DYNAMIC','STO_DYNAMIC','-append'); if ~options_MC.no_identification_reducedform IDE_REDUCEDFORM.si_dREDUCEDFORMnorm = si_dREDUCEDFORMnorm; save([IdentifDirectoryName '/' fname '_identif.mat'], 'IDE_REDUCEDFORM', 'STO_REDUCEDFORM','-append'); end if ~options_MC.no_identification_moments IDE_MOMENTS.si_dMOMENTSnorm = si_dMOMENTSnorm; save([IdentifDirectoryName '/' fname '_identif.mat'], 'IDE_MOMENTS', 'STO_MOMENTS','-append'); end end else %% load previous analysis load([IdentifDirectoryName '/' fname '_identif']); parameters = store_options_ident.parameter_set; options_ident.parameter_set = parameters; options_ident.prior_mc = size(pdraws,1); SampleSize = options_ident.prior_mc; options_.options_ident = options_ident; end %% if identification.run 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 = []; 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),:)); if ~options_ident.no_identification_reducedform STO_si_dREDUCEDFORM = cat(3,STO_si_dREDUCEDFORM, STO_si_dREDUCEDFORM(:,abs(iload),:)); end if ~options_ident.no_identification_moments STO_si_dMOMENTS = cat(3,STO_si_dMOMENTS, STO_si_dMOMENTS(:,abs(iload),:)); end if ~options_ident.no_identification_spectrum STO_dSPECTRUM = cat(3,STO_dSPECTRUM, STO_dSPECTRUM(:,abs(iload),:)); end if ~options_ident.no_identification_minimal STO_dMINIMAL = cat(3,STO_dMINIMAL, STO_dMINIMAL(:,abs(iload),:)); end end end if iload %if previous analysis is loaded fprintf(['Testing %s\n',parameters]); identification.display(ide_hess_point.params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident); if ~options_.nograph && ~error_indicator_point.identification_strength && ~error_indicator_point.identification_moments % plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs identification.plot(M_,ide_hess_point.params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, ... IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, [], name_tex); end end %displaying and plotting of results for MC sample if SampleSize > 1 fprintf('\nTesting MC sample\n'); %print results to console but make sure advanced=0 advanced0 = options_ident.advanced; options_ident.advanced = 0; identification.display(pdraws, IDE_REDUCEDFORM, IDE_MOMENTS, IDE_SPECTRUM, IDE_MINIMAL, name, options_ident); options_ident.advanced = advanced0; % reset advanced setting if ~options_.nograph && isfield(ide_hess_point,'ide_strength_dMOMENTS') % plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs identification.plot(M_, pdraws, IDE_MOMENTS, ide_hess_point, IDE_REDUCEDFORM, IDE_DYNAMIC, options_ident.advanced, 'MC sample ', name, ... IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, [], name_tex); end %advanced display and plots for MC Sample, i.e. look at draws with highest/lowest condition number if options_ident.advanced jcrit = find(IDE_MOMENTS.ino); if length(jcrit) < SampleSize if isempty(jcrit) % Make sure there is no overflow of plots produced (these are saved to the disk anyways) store_nodisplay = options_.nodisplay; options_.nodisplay = 1; % HIGHEST CONDITION NUMBER [~, jmax] = max(IDE_MOMENTS.cond); tittxt = 'Draw with HIGHEST condition number'; 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, error_indicator_max] = ... identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,pdraws(jmax,:), indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end initializes some persistent variables save([IdentifDirectoryName '/' fname '_identif.mat'], 'ide_hess_max', 'ide_moments_max', 'ide_spectrum_max', 'ide_minimal_max','ide_reducedform_max', 'ide_dynamic_max', 'jmax', '-append'); end advanced0 = options_ident.advanced; options_ident.advanced = 1; % make sure advanced setting is on identification.display(pdraws(jmax,:), ide_reducedform_max, ide_moments_max, ide_spectrum_max, ide_minimal_max, name, options_ident); options_ident.advanced = advanced0; %reset advanced setting if ~options_.nograph && ~error_indicator_max.identification_strength && ~error_indicator_max.identification_moments % plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs identification.plot(M_, pdraws(jmax,:), ide_moments_max, ide_hess_max, ide_reducedform_max, ide_dynamic_max, 1, tittxt, name, ... IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, tittxt, name_tex); end % SMALLEST condition number [~, jmin] = min(IDE_MOMENTS.cond); tittxt = 'Draw with SMALLEST condition number'; 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, ~, ~, error_indicator_min] = ... identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,pdraws(jmin,:), indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end initializes persistent variables save([IdentifDirectoryName '/' fname '_identif.mat'], 'ide_hess_min', 'ide_moments_min','ide_spectrum_min','ide_minimal_min','ide_reducedform_min', 'ide_dynamic_min', 'jmin', '-append'); end advanced0 = options_ident.advanced; options_ident.advanced = 1; % make sure advanced setting is on identification.display(pdraws(jmin,:), ide_reducedform_min, ide_moments_min, ide_spectrum_min, ide_minimal_min, name, options_ident); options_ident.advanced = advanced0; %reset advanced setting if ~options_.nograph && ~error_indicator_min.identification_strength && ~error_indicator_min.identification_moments % plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs identification.plot(M_, pdraws(jmin,:),ide_moments_min,ide_hess_min,ide_reducedform_min,ide_dynamic_min,1,tittxt,name,... IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, tittxt,name_tex); end % reset nodisplay option options_.nodisplay = store_nodisplay; else % Make sure there is no overflow of plots produced (these are saved to the disk anyways) store_nodisplay = options_.nodisplay; options_.nodisplay = 1; for j=1:length(jcrit) 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, error_indicator_j] = ... identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,pdraws(jcrit(j),:), indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); end advanced0 = options_ident.advanced; options_ident.advanced = 1; %make sure advanced setting is on identification.display(pdraws(jcrit(j),:), ide_reducedform_(j), ide_moments_(j), ide_spectrum_(j), ide_minimal_(j), name, options_ident); options_ident.advanced = advanced0; % reset advanced if ~options_.nograph && ~error_indicator_j.identification_strength && ~error_indicator_j.identification_moments % plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs identification.plot(M_, pdraws(jcrit(j),:), ide_moments_(j), ide_hess_(j), ide_reducedform_(j), ide_dynamic_(j), 1, tittxt, name, ... IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, tittxt, name_tex); end end if ~iload save([IdentifDirectoryName '/' fname '_identif.mat'], 'ide_hess_', 'ide_moments_', 'ide_reducedform_', 'ide_dynamic_', 'ide_spectrum_', 'ide_minimal_', 'jcrit', '-append'); end % reset nodisplay option options_.nodisplay = store_nodisplay; end end end end %reset warning state warning_config; fprintf('\n==== Identification analysis completed ====\n\n')