594 lines
23 KiB
Matlab
594 lines
23 KiB
Matlab
function [pdraws, TAU, GAM, LRE, gp, H, JJ] = dynare_identification(options_ident, pdraws0)
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%function [pdraws, TAU, GAM, LRE, gp, H, JJ] = dynare_identification(options_ident, pdraws0)
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%
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% INPUTS
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% o options_ident [structure] identification options
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% o pdraws0 [matrix] optional: matrix of MC sample of model params.
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%
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% OUTPUTS
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% o pdraws [matrix] matrix of MC sample of model params used
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% o TAU, [matrix] MC sample of entries in the model solution (stacked vertically)
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% o GAM, [matrix] MC sample of entries in the moments (stacked vertically)
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% o LRE, [matrix] MC sample of entries in LRE model (stacked vertically)
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% o gp, [matrix] derivatives of the Jacobian (LRE model)
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% o H, [matrix] derivatives of the model solution
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% o JJ [matrix] derivatives of the moments
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%
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% SPECIAL REQUIREMENTS
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% None
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% main
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%
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% Copyright (C) 2010-2013 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global M_ options_ oo_ bayestopt_ estim_params_
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if exist('OCTAVE_VERSION')
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warning('off'),
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else
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warning off,
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end
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fname_ = M_.fname;
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if ~isfield(M_,'dname'),
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M_.dname = M_.fname;
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end
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options_ident = set_default_option(options_ident,'gsa_sample_file',0);
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options_ident = set_default_option(options_ident,'parameter_set','prior_mean');
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options_ident = set_default_option(options_ident,'load_ident_files',0);
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options_ident = set_default_option(options_ident,'useautocorr',0);
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options_ident = set_default_option(options_ident,'ar',1);
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options_ident = set_default_option(options_ident,'prior_mc',1);
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options_ident = set_default_option(options_ident,'prior_range',0);
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options_ident = set_default_option(options_ident,'periods',300);
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options_ident = set_default_option(options_ident,'replic',100);
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options_ident = set_default_option(options_ident,'advanced',0);
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options_ident = set_default_option(options_ident,'normalize_jacobians',1);
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options_ident = set_default_option(options_ident,'lik_init',1);
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options_ident = set_default_option(options_ident,'analytic_derivation',1);
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if isfield(options_ident,'nograph'),
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options_.nograph=options_ident.nograph;
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end
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if isfield(options_ident,'nodisplay'),
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options_.nodisplay=options_ident.nodisplay;
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end
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if isfield(options_ident,'graph_format'),
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options_.graph_format=options_ident.graph_format;
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end
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if options_ident.gsa_sample_file,
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GSAFolder = checkpath('gsa',M_.dname);
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if options_ident.gsa_sample_file==1,
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load([GSAFolder,filesep,fname_,'_prior'],'lpmat','lpmat0','istable');
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elseif options_ident.gsa_sample_file==2,
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load([GSAFolder,filesep,fname_,'_mc'],'lpmat','lpmat0','istable');
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else
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load([GSAFolder,filesep,options_ident.gsa_sample_file],'lpmat','lpmat0','istable');
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end
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if isempty(istable),
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istable=1:size(lpmat,1);
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end
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if ~isempty(lpmat0),
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lpmatx=lpmat0(istable,:);
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else
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lpmatx=[];
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end
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pdraws0 = [lpmatx lpmat(istable,:)];
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clear lpmat lpmat0 istable;
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elseif nargin==1,
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pdraws0=[];
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end
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external_sample=0;
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if nargin==2 || ~isempty(pdraws0),
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options_ident.prior_mc=size(pdraws0,1);
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options_ident.load_ident_files = 0;
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external_sample=1;
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end
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if isempty(estim_params_),
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options_ident.prior_mc=1;
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options_ident.prior_range=0;
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prior_exist=0;
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else
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prior_exist=1;
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parameters = options_ident.parameter_set;
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end
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% options_ident.load_ident_files=1;
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iload = options_ident.load_ident_files;
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%options_ident.advanced=1;
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advanced = options_ident.advanced;
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nlags = options_ident.ar;
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periods = options_ident.periods;
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replic = options_ident.replic;
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useautocorr = options_ident.useautocorr;
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options_.order=1;
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options_.ar=nlags;
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options_.prior_mc = options_ident.prior_mc;
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options_.options_ident = options_ident;
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options_.Schur_vec_tol = 1.e-8;
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options_.nomoments=0;
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options_.analytic_derivation=1;
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options_ = set_default_option(options_,'datafile','');
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options_.mode_compute = 0;
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options_.plot_priors = 0;
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options_.smoother=1;
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[dataset_,xparam1, M_, options_, oo_, estim_params_,bayestopt_]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
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options_ident.analytic_derivation_mode = options_.analytic_derivation_mode;
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if isempty(dataset_),
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dataset_.info.ntobs = periods;
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dataset_.info.nvobs = rows(options_.varobs);
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dataset_.info.varobs = options_.varobs;
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dataset_.rawdata = [];
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dataset_.missing.state = 0;
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for jdata=1:periods,
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temp1{jdata}=[1:dataset_.info.nvobs]';
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end
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dataset_.missing.aindex = temp1;
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dataset_.missing.vindex = [];
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dataset_.missing.number_of_observations = [];
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dataset_.missing.no_more_missing_observations = 1;
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dataset_.descriptive.mean = [];
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dataset_.data = [];
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% data_info.gend = periods;
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% data_info.data = [];
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% data_info.data_index = [];
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% data_info.number_of_observations = periods*size(options_.varobs,1);
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% data_info.no_more_missing_observations = 0;
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% data_info.missing_value = 0;
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end
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% results = prior_sampler(0,M_,bayestopt_,options_,oo_);
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if prior_exist
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if any(bayestopt_.pshape > 0)
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if options_ident.prior_range
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prior_draw(1,1);
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else
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prior_draw(1);
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end
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else
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options_ident.prior_mc=1;
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end
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end
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SampleSize = options_ident.prior_mc;
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if ~(exist('sylvester3','file')==2),
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dynareroot = strrep(which('dynare'),'dynare.m','');
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addpath([dynareroot 'gensylv'])
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end
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IdentifDirectoryName = CheckPath('identification',M_.dname);
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if prior_exist,
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indx = [];
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if ~isempty(estim_params_.param_vals),
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indx = estim_params_.param_vals(:,1);
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end
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indexo=[];
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if ~isempty(estim_params_.var_exo)
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indexo = estim_params_.var_exo(:,1);
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end
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nparam = length(bayestopt_.name);
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np = estim_params_.np;
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name = bayestopt_.name;
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name_tex = char(M_.exo_names_tex(indexo,:),M_.param_names_tex(indx,:));
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offset = estim_params_.nvx;
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offset = offset + estim_params_.nvn;
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offset = offset + estim_params_.ncx;
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offset = offset + estim_params_.ncn;
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else
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indx = [1:M_.param_nbr];
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indexo = [1:M_.exo_nbr];
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offset = M_.exo_nbr;
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np = M_.param_nbr;
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nparam = np+offset;
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name = [cellstr(M_.exo_names); cellstr(M_.param_names)];
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name_tex = [cellstr(M_.exo_names_tex); cellstr(M_.param_names_tex)];
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end
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options_ident = set_default_option(options_ident,'max_dim_cova_group',min([2,nparam-1]));
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options_ident.max_dim_cova_group = min([options_ident.max_dim_cova_group,nparam-1]);
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MaxNumberOfBytes=options_.MaxNumberOfBytes;
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store_options_ident = options_ident;
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disp(' ')
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disp(['==== Identification analysis ====' ]),
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disp(' ')
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if iload <=0,
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[I,J]=find(M_.lead_lag_incidence');
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if prior_exist,
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% if exist([fname_,'_mean.mat'],'file'),
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% % disp('Testing posterior mean')
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% load([fname_,'_mean'],'xparam1')
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% pmean = xparam1';
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% clear xparam1
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% end
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% if exist([fname_,'_mode.mat'],'file'),
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% % disp('Testing posterior mode')
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% load([fname_,'_mode'],'xparam1')
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% pmode = xparam1';
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% clear xparam1
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% end
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params = set_prior(estim_params_,M_,options_)';
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if all(bayestopt_.pshape == 0)
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parameters = 'ML_Starting_value';
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disp('Testing ML Starting value')
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else
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switch parameters
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case 'posterior_mode'
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disp('Testing posterior mode')
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params(1,:) = get_posterior_parameters('mode');
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case 'posterior_mean'
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disp('Testing posterior mean')
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params(1,:) = get_posterior_parameters('mean');
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case 'posterior_median'
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disp('Testing posterior median')
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params(1,:) = get_posterior_parameters('median');
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case 'prior_mode'
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disp('Testing prior mode')
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params(1,:) = bayestopt_.p5(:);
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case 'prior_mean'
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disp('Testing prior mean')
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params(1,:) = bayestopt_.p1;
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otherwise
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disp('The option parameter_set has to be equal to:')
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disp(' ''posterior_mode'', ')
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disp(' ''posterior_mean'', ')
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disp(' ''posterior_median'', ')
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disp(' ''prior_mode'' or')
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disp(' ''prior_mean''.')
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error;
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end
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end
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else
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params = [sqrt(diag(M_.Sigma_e))', M_.params'];
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parameters = 'Current_params';
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disp('Testing current parameter values')
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end
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[idehess_point, idemoments_point, idemodel_point, idelre_point, derivatives_info_point, info] = ...
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identification_analysis(params,indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
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if info(1)~=0,
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disp(' ')
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disp('----------- ')
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disp('Parameter error:')
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disp(['The model does not solve for ', parameters, ' with error code info = ', int2str(info(1))]),
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disp(' ')
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if info(1)==1,
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disp('info==1 %! The model doesn''t determine the current variables uniquely.')
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elseif info(1)==2,
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disp('info==2 %! MJDGGES returned an error code.')
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elseif info(1)==3,
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disp('info==3 %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium. ')
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elseif info(1)==4,
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disp('info==4 %! Blanchard & Kahn conditions are not satisfied: indeterminacy. ')
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elseif info(1)==5,
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disp('info==5 %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure. ')
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elseif info(1)==6,
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disp('info==6 %! The jacobian evaluated at the deterministic steady state is complex.')
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elseif info(1)==19,
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disp('info==19 %! The steadystate routine thrown an exception (inconsistent deep parameters). ')
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elseif info(1)==20,
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disp('info==20 %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations). ')
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elseif info(1)==21,
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disp('info==21 %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.')
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elseif info(1)==22,
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disp('info==22 %! The steady has NaNs. ')
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elseif info(1)==23,
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disp('info==23 %! M_.params has been updated in the steadystate routine and has complex valued scalars. ')
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elseif info(1)==24,
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disp('info==24 %! M_.params has been updated in the steadystate routine and has some NaNs. ')
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elseif info(1)==30,
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disp('info==30 %! Ergodic variance can''t be computed. ')
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end
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disp('----------- ')
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disp(' ')
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if any(bayestopt_.pshape)
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disp('Try sampling up to 50 parameter sets from the prior.')
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kk=0;
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while kk<50 && info(1),
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kk=kk+1;
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params = prior_draw();
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[idehess_point, idemoments_point, idemodel_point, idelre_point, derivatives_info_point, info] = ...
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identification_analysis(params,indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
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end
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end
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if info(1)
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disp(' ')
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disp('----------- ')
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disp('Identification stopped:')
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if any(bayestopt_.pshape)
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disp('The model did not solve for any of 50 attempts of random samples from the prior')
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end
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disp('----------- ')
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disp(' ')
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return,
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end
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else
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idehess_point.params=params;
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% siH = idemodel_point.siH;
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% siJ = idemoments_point.siJ;
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% siLRE = idelre_point.siLRE;
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% normH = max(abs(siH)')';
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% normJ = max(abs(siJ)')';
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% normLRE = max(abs(siLRE)')';
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save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_point', 'idemoments_point','idemodel_point', 'idelre_point','store_options_ident')
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disp_identification(params, idemodel_point, idemoments_point, name, advanced);
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if ~options_.nograph,
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plot_identification(params,idemoments_point,idehess_point,idemodel_point,idelre_point,advanced,parameters,name,IdentifDirectoryName);
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end
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end
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if SampleSize > 1,
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disp(' ')
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disp('Monte Carlo Testing')
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h = dyn_waitbar(0,'Monte Carlo identification checks ...');
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iteration = 0;
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loop_indx = 0;
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file_index = 0;
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run_index = 0;
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options_MC=options_ident;
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options_MC.advanced=0;
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else
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iteration = 1;
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pdraws = [];
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end
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while iteration < SampleSize,
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loop_indx = loop_indx+1;
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if external_sample,
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params = pdraws0(iteration+1,:);
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else
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params = prior_draw();
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end
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[dum1, ideJ, ideH, ideGP, dum2 , info] = ...
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identification_analysis(params,indx,indexo,options_MC,dataset_, prior_exist, name_tex,0);
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if iteration==0 && info(1)==0,
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MAX_tau = min(SampleSize,ceil(MaxNumberOfBytes/(size(ideH.siH,1)*nparam)/8));
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stoH = zeros([size(ideH.siH,1),nparam,MAX_tau]);
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stoJJ = zeros([size(ideJ.siJ,1),nparam,MAX_tau]);
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stoLRE = zeros([size(ideGP.siLRE,1),np,MAX_tau]);
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TAU = zeros(size(ideH.siH,1),SampleSize);
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GAM = zeros(size(ideJ.siJ,1),SampleSize);
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LRE = zeros(size(ideGP.siLRE,1),SampleSize);
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pdraws = zeros(SampleSize,nparam);
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idemoments.indJJ = ideJ.indJJ;
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idemodel.indH = ideH.indH;
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idelre.indLRE = ideGP.indLRE;
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idemoments.ind0 = zeros(SampleSize,nparam);
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idemodel.ind0 = zeros(SampleSize,nparam);
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idelre.ind0 = zeros(SampleSize,np);
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idemoments.jweak = zeros(SampleSize,nparam);
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idemodel.jweak = zeros(SampleSize,nparam);
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idelre.jweak = zeros(SampleSize,np);
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idemoments.jweak_pair = zeros(SampleSize,nparam*(nparam+1)/2);
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idemodel.jweak_pair = zeros(SampleSize,nparam*(nparam+1)/2);
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idelre.jweak_pair = zeros(SampleSize,np*(np+1)/2);
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idemoments.cond = zeros(SampleSize,1);
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idemodel.cond = zeros(SampleSize,1);
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idelre.cond = zeros(SampleSize,1);
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idemoments.Mco = zeros(SampleSize,nparam);
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idemodel.Mco = zeros(SampleSize,nparam);
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idelre.Mco = zeros(SampleSize,np);
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idemoments.S = zeros(SampleSize,min(8,nparam));
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idemoments.V = zeros(SampleSize,nparam,min(8,nparam));
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delete([IdentifDirectoryName '/' M_.fname '_identif_*.mat'])
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end
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if info(1)==0,
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iteration = iteration + 1;
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run_index = run_index + 1;
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TAU(:,iteration)=ideH.TAU;
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LRE(:,iteration)=ideGP.LRE;
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GAM(:,iteration)=ideJ.GAM;
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idemoments.cond(iteration,1)=ideJ.cond;
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idemodel.cond(iteration,1)=ideH.cond;
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idelre.cond(iteration,1)=ideGP.cond;
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idemoments.ino(iteration,1)=ideJ.ino;
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idemodel.ino(iteration,1)=ideH.ino;
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idelre.ino(iteration,1)=ideGP.ino;
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idemoments.ind0(iteration,:)=ideJ.ind0;
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idemodel.ind0(iteration,:)=ideH.ind0;
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idelre.ind0(iteration,:)=ideGP.ind0;
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idemoments.jweak(iteration,:)=ideJ.jweak;
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idemodel.jweak(iteration,:)=ideH.jweak;
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idelre.jweak(iteration,:)=ideGP.jweak;
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idemoments.jweak_pair(iteration,:)=ideJ.jweak_pair;
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idemodel.jweak_pair(iteration,:)=ideH.jweak_pair;
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idelre.jweak_pair(iteration,:)=ideGP.jweak_pair;
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idemoments.Mco(iteration,:)=ideJ.Mco;
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idemodel.Mco(iteration,:)=ideH.Mco;
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idelre.Mco(iteration,:)=ideGP.Mco;
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idemoments.S(iteration,:)=ideJ.S;
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idemoments.V(iteration,:,:)=ideJ.V;
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stoLRE(:,:,run_index) = ideGP.siLRE;
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stoH(:,:,run_index) = ideH.siH;
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stoJJ(:,:,run_index) = ideJ.siJ;
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pdraws(iteration,:) = params;
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if run_index==MAX_tau || iteration==SampleSize,
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file_index = file_index + 1;
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if run_index<MAX_tau,
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stoH = stoH(:,:,1:run_index);
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stoJJ = stoJJ(:,:,1:run_index);
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stoLRE = stoLRE(:,:,1:run_index);
|
|
end
|
|
save([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index) '.mat'], 'stoH', 'stoJJ', 'stoLRE')
|
|
run_index = 0;
|
|
stoH = zeros(size(stoH));
|
|
stoJJ = zeros(size(stoJJ));
|
|
stoLRE = zeros(size(stoLRE));
|
|
|
|
end
|
|
|
|
if SampleSize > 1,
|
|
% if exist('OCTAVE_VERSION') || options_.console_mode,
|
|
% console_waitbar(0,iteration/SampleSize);
|
|
% else
|
|
dyn_waitbar(iteration/SampleSize,h,['MC identification checks ',int2str(iteration),'/',int2str(SampleSize)])
|
|
% end
|
|
end
|
|
end
|
|
|
|
end
|
|
|
|
|
|
if SampleSize > 1,
|
|
if exist('OCTAVE_VERSION') || options_.console_mode,
|
|
fprintf('\n');
|
|
diary on;
|
|
else
|
|
close(h),
|
|
end
|
|
normTAU=std(TAU')';
|
|
normLRE=std(LRE')';
|
|
normGAM=std(GAM')';
|
|
normaliz1=std(pdraws);
|
|
iter=0;
|
|
for ifile_index = 1:file_index,
|
|
load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(ifile_index) '.mat'], 'stoH', 'stoJJ', 'stoLRE')
|
|
for irun=1:size(stoH,3),
|
|
iter=iter+1;
|
|
siJnorm(iter,:) = vnorm(stoJJ(:,:,irun)./repmat(normGAM,1,nparam)).*normaliz1;
|
|
siHnorm(iter,:) = vnorm(stoH(:,:,irun)./repmat(normTAU,1,nparam)).*normaliz1;
|
|
siLREnorm(iter,:) = vnorm(stoLRE(:,:,irun)./repmat(normLRE,1,nparam-offset)).*normaliz1(offset+1:end);
|
|
end
|
|
|
|
end
|
|
idemoments.siJnorm = siJnorm;
|
|
idemodel.siHnorm = siHnorm;
|
|
idelre.siLREnorm = siLREnorm;
|
|
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'pdraws', 'idemodel', 'idemoments', 'idelre', ... %'indJJ', 'indH', 'indLRE', ...
|
|
'TAU', 'GAM', 'LRE','-append')
|
|
else
|
|
siJnorm = idemoments_point.siJnorm;
|
|
siHnorm = idemodel_point.siHnorm;
|
|
siLREnorm = idelre_point.siLREnorm;
|
|
end
|
|
|
|
else
|
|
load([IdentifDirectoryName '/' M_.fname '_identif'])
|
|
% identFiles = dir([IdentifDirectoryName '/' M_.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 nargout>3 && iload,
|
|
filnam = dir([IdentifDirectoryName '/' M_.fname '_identif_*.mat']);
|
|
H=[];
|
|
JJ = [];
|
|
gp = [];
|
|
for j=1:length(filnam),
|
|
load([IdentifDirectoryName '/' M_.fname '_identif_',int2str(j),'.mat']);
|
|
H = cat(3,H, stoH(:,abs(iload),:));
|
|
JJ = cat(3,JJ, stoJJ(:,abs(iload),:));
|
|
gp = cat(3,gp, stoLRE(:,abs(iload),:));
|
|
|
|
end
|
|
end
|
|
|
|
if iload,
|
|
disp(['Testing ',parameters])
|
|
disp_identification(idehess_point.params, idemodel_point, idemoments_point, name,advanced);
|
|
if ~options_.nograph,
|
|
plot_identification(idehess_point.params,idemoments_point,idehess_point,idemodel_point,idelre_point,advanced,parameters,name,IdentifDirectoryName);
|
|
end
|
|
end
|
|
if SampleSize > 1,
|
|
fprintf('\n')
|
|
disp('Testing MC sample')
|
|
disp_identification(pdraws, idemodel, idemoments, name);
|
|
if ~options_.nograph,
|
|
plot_identification(pdraws,idemoments,idehess_point,idemodel,idelre,advanced,'MC sample - ',name, IdentifDirectoryName);
|
|
end
|
|
if advanced,
|
|
jcrit=find(idemoments.ino);
|
|
if length(jcrit)<SampleSize,
|
|
if isempty(jcrit),
|
|
[dum,jmax]=max(idemoments.cond);
|
|
fprintf('\n')
|
|
tittxt = 'Draw with HIGHEST condition number';
|
|
fprintf('\n')
|
|
disp(['Testing ',tittxt, '. Press ENTER']), pause(5),
|
|
if ~iload,
|
|
[idehess_max, idemoments_max, idemodel_max, idelre_max, derivatives_info_max] = ...
|
|
identification_analysis(pdraws(jmax,:),indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
|
|
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_max', 'idemoments_max','idemodel_max', 'idelre_max', 'jmax', '-append');
|
|
end
|
|
disp_identification(pdraws(jmax,:), idemodel_max, idemoments_max, name,1);
|
|
close all,
|
|
if ~options_.nograph,
|
|
plot_identification(pdraws(jmax,:),idemoments_max,idehess_max,idemodel_max,idelre_max,1,tittxt,name,IdentifDirectoryName);
|
|
end
|
|
[dum,jmin]=min(idemoments.cond);
|
|
fprintf('\n')
|
|
tittxt = 'Draw with SMALLEST condition number';
|
|
fprintf('\n')
|
|
disp(['Testing ',tittxt, '. Press ENTER']), pause(5),
|
|
if ~iload,
|
|
[idehess_min, idemoments_min, idemodel_min, idelre_min, derivatives_info_min] = ...
|
|
identification_analysis(pdraws(jmin,:),indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
|
|
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_min', 'idemoments_min','idemodel_min', 'idelre_min', 'jmin', '-append');
|
|
end
|
|
disp_identification(pdraws(jmin,:), idemodel_min, idemoments_min, name,1);
|
|
close all,
|
|
if ~options_.nograph,
|
|
plot_identification(pdraws(jmin,:),idemoments_min,idehess_min,idemodel_min,idelre_min,1,tittxt,name,IdentifDirectoryName);
|
|
end
|
|
else
|
|
for j=1:length(jcrit),
|
|
tittxt = ['Rank deficient draw n. ',int2str(j)];
|
|
fprintf('\n')
|
|
disp(['Testing ',tittxt, '. Press ENTER']), pause(5),
|
|
if ~iload,
|
|
[idehess_(j), idemoments_(j), idemodel_(j), idelre_(j), derivatives_info_(j)] = ...
|
|
identification_analysis(pdraws(jcrit(j),:),indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
|
|
end
|
|
disp_identification(pdraws(jcrit(j),:), idemodel_(j), idemoments_(j), name,1);
|
|
close all,
|
|
if ~options_.nograph,
|
|
plot_identification(pdraws(jcrit(j),:),idemoments_(j),idehess_(j),idemodel_(j),idelre_(j),1,tittxt,name,IdentifDirectoryName);
|
|
end
|
|
end
|
|
if ~iload,
|
|
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_', 'idemoments_','idemodel_', 'idelre_', 'jcrit', '-append');
|
|
end
|
|
end
|
|
end
|
|
end
|
|
end
|
|
|
|
if exist('OCTAVE_VERSION')
|
|
warning('on'),
|
|
else
|
|
warning on,
|
|
end
|
|
|
|
disp(' ')
|
|
disp(['==== Identification analysis completed ====' ]),
|
|
disp(' ')
|
|
disp(' ')
|