Select Git revision
Forked from
Dynare / dynare
158 commits behind the upstream repository.
run.m 59.36 KiB
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 <https://www.gnu.org/licenses/>.% =========================================================================
fname = M_.fname; %model name
dname = M_.dname; %model name
%turn warnings off, either globally or only relevant ids
orig_warning_state = warning;
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 computationsoptions_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_info, ~, ~, M_, options_, oo_, estim_params_, bayestopt_] = 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, ~, 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, ~, 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');
warning(orig_warning_state);
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_reducedform, ide_dynamic, ~, 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 && mod(iteration,3)
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, ~, ~, 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 statewarning(orig_warning_state);
fprintf('\n==== Identification analysis completed ====\n\n')