904 lines
36 KiB
Matlab
904 lines
36 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) 2010-2011 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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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',3);
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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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if nargin==2,
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options_ident.prior_mc=size(pdraws0,1);
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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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end
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iload = options_ident.load_ident_files;
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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_.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_ = 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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[data,rawdata,xparam1,data_info]=dynare_estimation_init([],fname_,1);
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if isempty(data_info),
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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*length(options_.varobs);
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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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SampleSize = options_ident.prior_mc;
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% results = prior_sampler(0,M_,bayestopt_,options_,oo_);
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if prior_exist
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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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end
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if ~(exist('sylvester3mr','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');
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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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MaxNumberOfBytes=options_.MaxNumberOfBytes;
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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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iteration = 0;
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burnin_iteration = 0;
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if SampleSize==1,
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BurninSampleSize=0;
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else
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BurninSampleSize=50;
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end
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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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if SampleSize > 1,
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h = waitbar(0,'Monte Carlo identification checks ...');
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end
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[I,J]=find(M_.lead_lag_incidence');
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while iteration < SampleSize,
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loop_indx = loop_indx+1;
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if prior_exist,
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if SampleSize==1,
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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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params = xparam1';
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clear xparam1
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elseif 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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params = xparam1';
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clear xparam1
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else
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disp('Testing prior mean')
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params = set_prior(estim_params_,M_,options_)';
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end
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else
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if nargin==2,
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if burnin_iteration>=BurninSampleSize,
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params = pdraws0(iteration+1,:);
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else
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params = pdraws0(burnin_iteration+1,:);
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end
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else
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params = prior_draw();
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end
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end
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set_all_parameters(params);
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else
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params = [sqrt(diag(M_.Sigma_e))', M_.params'];
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end
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[A,B,ys,info]=dynare_resolve;
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if info(1)==0,
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oo0=oo_;
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tau=[oo_.dr.ys(oo_.dr.order_var); vec(A); dyn_vech(B*M_.Sigma_e*B')];
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yy0=oo_.dr.ys(I);
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[residual, g1 ] = feval([M_.fname,'_dynamic'],yy0, ...
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oo_.exo_steady_state', M_.params, ...
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oo_.dr.ys, 1);
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if burnin_iteration<BurninSampleSize,
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burnin_iteration = burnin_iteration + 1;
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pdraws(burnin_iteration,:) = params;
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TAU(:,burnin_iteration)=tau;
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LRE(:,burnin_iteration)=[oo_.dr.ys(oo_.dr.order_var); vec(g1)];
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[gam,stationary_vars] = th_autocovariances(oo0.dr,bayestopt_.mfys,M_,options_);
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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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sdy = sqrt(diag(gam{1}));
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sy = sdy*sdy';
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if useautocorr,
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sy=sy-diag(diag(sy))+eye(length(sy));
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gam{1}=gam{1}./sy;
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else
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for j=1:nlags,
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gam{j+1}=gam{j+1}.*sy;
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end
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end
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dum = dyn_vech(gam{1});
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for j=1:nlags,
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dum = [dum; vec(gam{j+1})];
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end
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GAM(:,burnin_iteration)=[oo_.dr.ys(bayestopt_.mfys); dum];
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% warning warning_old_state;
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else
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iteration = iteration + 1;
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run_index = run_index + 1;
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if iteration==1 && BurninSampleSize,
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indJJ = (find(std(GAM')>1.e-8));
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indH = (find(std(TAU')>1.e-8));
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indLRE = (find(std(LRE')>1.e-8));
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TAU = zeros(length(indH),SampleSize);
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GAM = zeros(length(indJJ),SampleSize);
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LRE = zeros(length(indLRE),SampleSize);
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MAX_tau = min(SampleSize,ceil(MaxNumberOfBytes/(length(indH)*nparam)/8));
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MAX_gam = min(SampleSize,ceil(MaxNumberOfBytes/(length(indJJ)*nparam)/8));
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stoH = zeros([length(indH),nparam,MAX_tau]);
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stoJJ = zeros([length(indJJ),nparam,MAX_tau]);
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delete([IdentifDirectoryName '/' M_.fname '_identif_*.mat'])
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end
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end
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if iteration,
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[JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, M_,oo0,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
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derivatives_info.dA=dA;
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derivatives_info.dOm=dOm;
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derivatives_info.dYss=dYss;
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if BurninSampleSize == 0,
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indJJ = (find(max(abs(JJ'))>1.e-8));
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indH = (find(max(abs(H'))>1.e-8));
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indLRE = (find(max(abs(gp'))>1.e-8));
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TAU = zeros(length(indH),SampleSize);
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GAM = zeros(length(indJJ),SampleSize);
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LRE = zeros(length(indLRE),SampleSize);
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MAX_tau = min(SampleSize,ceil(MaxNumberOfBytes/(length(indH)*nparam)/8));
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MAX_gam = min(SampleSize,ceil(MaxNumberOfBytes/(length(indJJ)*nparam)/8));
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stoH = zeros([length(indH),nparam,MAX_tau]);
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stoJJ = zeros([length(indJJ),nparam,MAX_tau]);
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delete([IdentifDirectoryName '/' M_.fname '_identif_*.mat'])
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end
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TAU(:,iteration)=tau(indH);
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vg1 = [oo_.dr.ys(oo_.dr.order_var); vec(g1)];
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LRE(:,iteration)=vg1(indLRE);
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GAM(:,iteration)=gam(indJJ);
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stoLRE(:,:,run_index) = gp(indLRE,:);
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stoH(:,:,run_index) = H(indH,:);
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stoJJ(:,:,run_index) = JJ(indJJ,:);
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% use relative changes
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% siJ = abs(JJ(indJJ,:).*(1./gam(indJJ)*params));
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% siH = abs(H(indH,:).*(1./tau(indH)*params));
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% use prior uncertainty
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siJ = (JJ(indJJ,:));
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siH = (H(indH,:));
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siLRE = (gp(indLRE,:));
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% siJ = abs(JJ(indJJ,:).*(ones(length(indJJ),1)*bayestopt_.p2'));
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% siH = abs(H(indH,:).*(ones(length(indH),1)*bayestopt_.p2'));
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% siJ = abs(JJ(indJJ,:).*(1./mGAM'*bayestopt_.p2'));
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% siH = abs(H(indH,:).*(1./mTAU'*bayestopt_.p2'));
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siJnorm(iteration,:) = vnorm(siJ./repmat(GAM(:,iteration),1,nparam)).*params;
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siHnorm(iteration,:) = vnorm(siH./repmat(TAU(:,iteration),1,nparam)).*params;
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siLREnorm(iteration,:) = vnorm(siLRE./repmat(LRE(:,iteration),1,nparam-offset)).*params(offset+1:end);
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if iteration ==1,
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siJmean = abs(siJ)./SampleSize;
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siHmean = abs(siH)./SampleSize;
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siLREmean = abs(siLRE)./SampleSize;
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derJmean = (siJ)./SampleSize;
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derHmean = (siH)./SampleSize;
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derLREmean = (siLRE)./SampleSize;
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else
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siJmean = abs(siJ)./SampleSize+siJmean;
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siHmean = abs(siH)./SampleSize+siHmean;
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siLREmean = abs(siLRE)./SampleSize+siLREmean;
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derJmean = (siJ)./SampleSize+derJmean;
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derHmean = (siH)./SampleSize+derHmean;
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derLREmean = (siLRE)./SampleSize+derLREmean;
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end
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pdraws(iteration,:) = params;
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normH = max(abs(stoH(:,:,run_index))')';
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normJ = max(abs(stoJJ(:,:,run_index))')';
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normLRE = max(abs(stoLRE(:,:,run_index))')';
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% normH = TAU(:,iteration);
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% normJ = GAM(:,iteration);
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% normLRE = LRE(:,iteration);
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[idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), idelre.Mco(:,iteration), ...
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idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), idelre.Pco(:,:,iteration), ...
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idemodel.cond(iteration), idemoments.cond(iteration), idelre.cond(iteration), ...
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idemodel.ee(:,:,iteration), idemoments.ee(:,:,iteration), idelre.ee(:,:,iteration), ...
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idemodel.ind(:,iteration), idemoments.ind(:,iteration), ...
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idemodel.indno{iteration}, idemoments.indno{iteration}, ...
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idemodel.ino(iteration), idemoments.ino(iteration)] = ...
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identification_checks(H(indH,:)./normH(:,ones(nparam,1)),JJ(indJJ,:)./normJ(:,ones(nparam,1)), gp(indLRE,:)./normLRE(:,ones(size(gp,2),1)));
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% identification_checks(H(indH,:),JJ(indJJ,:), gp(indLRE,:), bayestopt_);
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indok = find(max(idemoments.indno{iteration},[],1)==0);
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if iteration ==1 && ~isempty(indok),
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ide_strength_J=NaN(1,nparam);
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ide_strength_J_prior=NaN(1,nparam);
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if advanced,
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[pars, cosnJ] = ident_bruteforce(JJ(indJJ,:)./normJ(:,ones(nparam,1)),2,1,name_tex);
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end
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normaliz = abs(params);
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if prior_exist,
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if ~isempty(estim_params_.var_exo),
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normaliz1 = estim_params_.var_exo(:,7); % normalize with prior standard deviation
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else
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normaliz1=[];
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end
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if ~isempty(estim_params_.param_vals),
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normaliz1 = [normaliz1; estim_params_.param_vals(:,7)]'; % normalize with prior standard deviation
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end
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% normaliz = max([normaliz; normaliz1]);
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else
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normaliz1 = ones(1,nparam);
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end
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replic = max([replic, length(indJJ)*3]);
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try,
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options_.irf = 0;
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options_.noprint = 1;
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options_.order = 1;
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options_.periods = data_info.gend+100;
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info = stoch_simul(options_.varobs);
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datax=oo_.endo_simul(options_.varobs_id,100+1:end);
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% datax=data;
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[fval,cost_flag,ys,trend_coeff,info,DLIK,AHess] = DsgeLikelihood(params',data_info.gend,datax,data_info.data_index,data_info.number_of_observations,derivatives_info);
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cparam = inv(-AHess);
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normaliz(indok) = sqrt(diag(cparam))';
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cmm = siJ*((-AHess)\siJ');
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catch,
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cmm = simulated_moment_uncertainty(indJJ, periods, replic);
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% Jinv=(siJ(:,indok)'*siJ(:,indok))\siJ(:,indok)';
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% MIM=inv(Jinv*cmm*Jinv');
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MIM=siJ(:,indok)'*(cmm\siJ(:,indok));
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deltaM = sqrt(diag(MIM));
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tildaM = MIM./((deltaM)*(deltaM'));
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rhoM=sqrt(1-1./diag(inv(tildaM)));
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deltaM = deltaM.*normaliz(indok)';
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normaliz(indok) = sqrt(diag(inv(MIM)))';
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end
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ide_strength_J(indok) = (1./(normaliz(indok)'./abs(params(indok)')));
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ide_strength_J_prior(indok) = (1./(normaliz(indok)'./normaliz1(indok)'));
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% indok = find(max(idemodel.indno{iteration},[],1)==0);
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% ide_strength_H(iteration,:)=zeros(1,nparam);
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% mim=inv(siH(:,indok)'*siH(:,indok))*siH(:,indok)';
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% % mim=mim*diag(GAM(:,iteration))*mim';
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% % MIM=inv(mim);
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% mim=mim.*repmat(TAU(:,iteration),1,length(indok))';
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% MIM=inv(mim*mim');
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% deltaM = sqrt(diag(MIM));
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% tildaM = MIM./((deltaM)*(deltaM'));
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% rhoM=sqrt(1-1./diag(inv(tildaM)));
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% deltaM = deltaM.*params(indok)';
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% ide_strength_H(iteration,indok) = (1./[sqrt(diag(inv(MIM)))./params(indok)']);
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% inok = find((abs(GAM(:,iteration))==0));
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% isok = find((abs(GAM(:,iteration))));
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% quant(isok,:) = siJ(isok,:)./repmat(GAM(isok,iteration),1,nparam);
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% quant(inok,:) = siJ(inok,:)./repmat(mean(abs(GAM(:,iteration))),length(inok),nparam);
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quant = siJ./repmat(sqrt(diag(cmm)),1,nparam);
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siJnorm(iteration,:) = vnorm(quant).*normaliz;
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% siJnorm(iteration,:) = vnorm(siJ(inok,:)).*normaliz;
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quant=[];
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inok = find((abs(TAU(:,iteration))==0));
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isok = find((abs(TAU(:,iteration))));
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quant(isok,:) = siH(isok,:)./repmat(TAU(isok,iteration),1,nparam);
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quant(inok,:) = siH(inok,:)./repmat(mean(abs(TAU(:,iteration))),length(inok),nparam);
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siHnorm(iteration,:) = vnorm(quant).*normaliz;
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% siHnorm(iteration,:) = vnorm(siH./repmat(TAU(:,iteration),1,nparam)).*normaliz;
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quant=[];
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inok = find((abs(LRE(:,iteration))==0));
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isok = find((abs(LRE(:,iteration))));
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quant(isok,:) = siLRE(isok,:)./repmat(LRE(isok,iteration),1,np);
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quant(inok,:) = siLRE(inok,:)./repmat(mean(abs(LRE(:,iteration))),length(inok),np);
|
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siLREnorm(iteration,:) = vnorm(quant).*normaliz(offset+1:end);
|
|
% siLREnorm(iteration,:) = vnorm(siLRE./repmat(LRE(:,iteration),1,nparam-offset)).*normaliz(offset+1:end);
|
|
end,
|
|
if run_index==MAX_tau || iteration==SampleSize,
|
|
file_index = file_index + 1;
|
|
if run_index<MAX_tau,
|
|
stoH = stoH(:,:,1:run_index);
|
|
stoJJ = stoJJ(:,:,1:run_index);
|
|
stoLRE = stoLRE(:,:,1:run_index);
|
|
end
|
|
save([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index) '.mat'], 'stoH', 'stoJJ', 'stoLRE')
|
|
run_index = 0;
|
|
|
|
end
|
|
|
|
if SampleSize > 1,
|
|
waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
|
|
end
|
|
end
|
|
end
|
|
end
|
|
|
|
|
|
if SampleSize > 1,
|
|
close(h)
|
|
end
|
|
|
|
|
|
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'pdraws', 'idemodel', 'idemoments', 'idelre', 'indJJ', 'indH', 'indLRE', ...
|
|
'siHmean', 'siJmean', 'siLREmean', 'derHmean', 'derJmean', 'derLREmean', 'TAU', 'GAM', 'LRE')
|
|
else
|
|
load([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', 'idelre', 'indJJ', 'indH', 'indLRE', ...
|
|
'siHmean', 'siJmean', 'siLREmean', 'derHmean', 'derJmean', 'derLREmean', 'TAU', 'GAM', 'LRE')
|
|
identFiles = dir([IdentifDirectoryName '/' M_.fname '_identif_*']);
|
|
options_ident.prior_mc=size(pdraws,1);
|
|
SampleSize = options_ident.prior_mc;
|
|
options_.options_ident = options_ident;
|
|
if iload>1,
|
|
idemodel0=idemodel;
|
|
idemoments0=idemoments;
|
|
idelre0 = idelre;
|
|
iteration = 0;
|
|
h = waitbar(0,'Monte Carlo identification checks ...');
|
|
for file_index=1:length(identFiles)
|
|
load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
|
|
for index=1:size(stoH,3),
|
|
iteration = iteration+1;
|
|
normH = max(abs(stoH(:,:,index))')';
|
|
normJ = max(abs(stoJJ(:,:,index))')';
|
|
normLRE = max(abs(stoLRE(:,:,index))')';
|
|
% normH = TAU(:,iteration);
|
|
% normJ = GAM(:,iteration);
|
|
% normLRE = LRE(:,iteration);
|
|
[idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), idelre.Mco(:,iteration), ...
|
|
idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), idelre.Pco(:,:,iteration), ...
|
|
idemodel.cond(iteration), idemoments.cond(iteration), idelre.cond(iteration), ...
|
|
idemodel.ee(:,:,iteration), idemoments.ee(:,:,iteration), idelre.ee(:,:,iteration), ...
|
|
idemodel.ind(:,iteration), idemoments.ind(:,iteration), ...
|
|
idemodel.indno{iteration}, idemoments.indno{iteration}, ...
|
|
idemodel.ino(iteration), idemoments.ino(iteration)] = ...
|
|
identification_checks(stoH(:,:,index)./normH(:,ones(nparam,1)), ...
|
|
stoJJ(:,:,index)./normJ(:,ones(nparam,1)), ...
|
|
stoLRE(:,:,index)./normLRE(:,ones(size(stoLRE,2),1)));
|
|
waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
|
|
end
|
|
end
|
|
close(h);
|
|
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idemodel', 'idemoments', 'idelre', '-append')
|
|
end
|
|
iteration = 0;
|
|
h = waitbar(0,'Monte Carlo identification checks ...');
|
|
for file_index=1:length(identFiles)
|
|
load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
|
|
for index=1:size(stoH,3),
|
|
iteration = iteration+1;
|
|
fobj(iteration)=sum(((GAM(:,iteration)-GAM(:,1))).^2);
|
|
fobjH(iteration)=sum(((TAU(:,iteration)-TAU(:,1))).^2);
|
|
fobjR(iteration)=sum(((GAM(:,iteration)./GAM(:,1)-1)).^2);
|
|
fobjHR(iteration)=sum(((TAU(:,iteration)./TAU(:,1)-1)).^2);
|
|
FOC(iteration,:) = (GAM(:,iteration)-GAM(:,1))'*stoJJ(:,:,index);
|
|
FOCH(iteration,:) = (TAU(:,iteration)-TAU(:,1))'*stoH(:,:,index);
|
|
FOCR(iteration,:) = ((GAM(:,iteration)./GAM(:,1)-1)./GAM(:,1))'*stoJJ(:,:,index);
|
|
FOCHR(iteration,:) = ((TAU(:,iteration)./TAU(:,1)-1)./TAU(:,1))'*stoH(:,:,index);
|
|
|
|
waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
|
|
end
|
|
end
|
|
close(h);
|
|
end
|
|
|
|
if SampleSize>1,
|
|
siJmean = siJmean.*(ones(length(indJJ),1)*std(pdraws));
|
|
siHmean = siHmean.*(ones(length(indH),1)*std(pdraws));
|
|
siLREmean = siLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
|
|
|
|
derJmean = derJmean.*(ones(length(indJJ),1)*std(pdraws));
|
|
derHmean = derHmean.*(ones(length(indH),1)*std(pdraws));
|
|
derLREmean = derLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
|
|
|
|
derHmean = abs(derHmean./(max(siHmean')'*ones(1,size(pdraws,2))));
|
|
derJmean = abs(derJmean./(max(siJmean')'*ones(1,size(pdraws,2))));
|
|
derLREmean = abs(derLREmean./(max(siLREmean')'*ones(1,np)));
|
|
|
|
siHmean = siHmean./(max(siHmean')'*ones(1,size(pdraws,2)));
|
|
siJmean = siJmean./(max(siJmean')'*ones(1,size(pdraws,2)));
|
|
siLREmean = siLREmean./(max(siLREmean')'*ones(1,np));
|
|
|
|
tstJmean = derJmean*0;
|
|
tstHmean = derHmean*0;
|
|
tstLREmean = derLREmean*0;
|
|
|
|
if exist('OCTAVE_VERSION')
|
|
warning('off'),
|
|
else
|
|
warning off,
|
|
end
|
|
|
|
for j=1:nparam,
|
|
indd = 1:length(siJmean(:,j));
|
|
tstJmean(indd,j) = abs(derJmean(indd,j))./siJmean(indd,j);
|
|
indd = 1:length(siHmean(:,j));
|
|
tstHmean(indd,j) = abs(derHmean(indd,j))./siHmean(indd,j);
|
|
if j>offset
|
|
indd = 1:length(siLREmean(:,j-offset));
|
|
tstLREmean(indd,j-offset) = abs(derLREmean(indd,j-offset))./siLREmean(indd,j-offset);
|
|
end
|
|
end
|
|
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
|
|
|
|
disp_identification(pdraws, idemodel, idemoments, name, advanced)
|
|
|
|
|
|
if advanced,
|
|
figure('Name','Identification LRE model form'),
|
|
subplot(211)
|
|
if SampleSize > 1,
|
|
mmm = mean(siLREnorm);
|
|
else
|
|
mmm = (siLREnorm);
|
|
end
|
|
[ss, is] = sort(mmm);
|
|
if SampleSize ==1,
|
|
bar(siLREnorm(:,is))
|
|
else
|
|
myboxplot(log(siLREnorm(:,is)))
|
|
end
|
|
% mmm = mean(siLREmean);
|
|
% [ss, is] = sort(mmm);
|
|
% myboxplot(siLREmean(:,is))
|
|
% set(gca,'ylim',[0 1.05])
|
|
set(gca,'xticklabel','')
|
|
for ip=1:np,
|
|
text(ip,-0.02,deblank(M_.param_names(indx(is(ip)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
title('Sensitivity in the LRE model')
|
|
|
|
subplot(212)
|
|
if SampleSize>1,
|
|
mmm = mean(-idelre.Mco');
|
|
else
|
|
mmm = (-idelre.Mco');
|
|
end
|
|
[ss, is] = sort(mmm);
|
|
if SampleSize ==1,
|
|
bar(idelre.Mco(is,:)')
|
|
else
|
|
myboxplot(idelre.Mco(is,:)')
|
|
end
|
|
set(gca,'ylim',[0 1])
|
|
set(gca,'xticklabel','')
|
|
for ip=1:np,
|
|
text(ip,-0.02,deblank(M_.param_names(indx(is(ip)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
title('Multicollinearity in the LRE model')
|
|
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_LRE'])
|
|
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_LRE']);
|
|
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_LRE']);
|
|
if options_.nograph, close(gcf); end
|
|
|
|
figure('Name','Identification in the model'),
|
|
% subplot(311)
|
|
%
|
|
% if SampleSize>1,
|
|
% mmm = mean(ide_strength_H);
|
|
% else
|
|
% mmm = (ide_strength_H);
|
|
% end
|
|
% [ss, is] = sort(mmm);
|
|
% if SampleSize>1,
|
|
% myboxplot(ide_strength_H(:,is))
|
|
% else
|
|
% bar(ide_strength_H(:,is))
|
|
% end
|
|
% % set(gca,'ylim',[0 1.05])
|
|
% set(gca,'xticklabel','')
|
|
% dy = get(gca,'ylim');
|
|
% % dy=dy(2)-dy(1);
|
|
% for ip=1:nparam,
|
|
% text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
|
% end
|
|
% title('Identification strength in the model')
|
|
|
|
subplot(211)
|
|
% mmm = mean(siHmean);
|
|
% [ss, is] = sort(mmm);
|
|
% myboxplot(siHmean(:,is))
|
|
if SampleSize>1,
|
|
mmm = mean(siHnorm);
|
|
else
|
|
mmm = (siHnorm);
|
|
end
|
|
[ss, is] = sort(mmm);
|
|
if SampleSize>1,
|
|
myboxplot(log(siHnorm(:,is)))
|
|
else
|
|
bar(siHnorm(:,is))
|
|
end
|
|
% set(gca,'ylim',[0 1.05])
|
|
set(gca,'xticklabel','')
|
|
dy = get(gca,'ylim');
|
|
% dy=dy(2)-dy(1);
|
|
for ip=1:nparam,
|
|
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
title('Sensitivity in the model')
|
|
|
|
subplot(212)
|
|
if SampleSize>1,
|
|
mmm = mean(-idemodel.Mco');
|
|
else
|
|
mmm = (-idemodel.Mco');
|
|
end
|
|
% [ss, is] = sort(mmm);
|
|
if SampleSize>1,
|
|
myboxplot(idemodel.Mco(is,:)')
|
|
else
|
|
bar(idemodel.Mco(is,:)')
|
|
end
|
|
set(gca,'ylim',[0 1])
|
|
set(gca,'xticklabel','')
|
|
for ip=1:nparam,
|
|
text(ip,-0.02,name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
title('Multicollinearity in the model')
|
|
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_model'])
|
|
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_model']);
|
|
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_model']);
|
|
if options_.nograph, close(gcf); end
|
|
end
|
|
|
|
figure('Name','Identification in the moments'),
|
|
subplot(211)
|
|
% mmm = mean(siHmean);
|
|
% [ss, is] = sort(mmm);
|
|
% myboxplot(siHmean(:,is))
|
|
if SampleSize>1,
|
|
mmm = mean(ide_strength_J);
|
|
else
|
|
mmm = (ide_strength_J);
|
|
end
|
|
[ss, is] = sort(mmm);
|
|
if SampleSize>1,
|
|
myboxplot(log(ide_strength_J(:,is)))
|
|
else
|
|
bar(log([ide_strength_J(:,is)' ide_strength_J_prior(:,is)']))
|
|
end
|
|
% set(gca,'ylim',[0 1.05])
|
|
set(gca,'xlim',[0 nparam+1])
|
|
set(gca,'xticklabel','')
|
|
dy = get(gca,'ylim');
|
|
% dy=dy(2)-dy(1);
|
|
for ip=1:nparam,
|
|
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
legend('relative to param value','relative to prior std','Location','Best')
|
|
title('Identification strength in the moments (log-scale)')
|
|
|
|
subplot(212)
|
|
% mmm = mean(siJmean);
|
|
% [ss, is] = sort(mmm);
|
|
% myboxplot(siJmean(:,is))
|
|
if SampleSize > 1,
|
|
mmm = mean(siJnorm);
|
|
else
|
|
mmm = (siJnorm);
|
|
end
|
|
% [ss, is] = sort(mmm);
|
|
if SampleSize > 1,
|
|
myboxplot(log(siJnorm(:,is)))
|
|
else
|
|
bar(siJnorm(:,is))
|
|
end
|
|
% set(gca,'ylim',[0 1.05])
|
|
set(gca,'xlim',[0 nparam+1])
|
|
set(gca,'xticklabel','')
|
|
dy = get(gca,'ylim');
|
|
% dy=dy(2)-dy(1);
|
|
for ip=1:nparam,
|
|
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
title('Sensitivity in the moments')
|
|
|
|
% subplot(313)
|
|
% if SampleSize>1,
|
|
% mmm = mean(-idemoments.Mco');
|
|
% else
|
|
% mmm = (-idemoments.Mco');
|
|
% end
|
|
% % [ss, is] = sort(mmm);
|
|
% if SampleSize>1,
|
|
% myboxplot(idemoments.Mco(is,:)')
|
|
% else
|
|
% bar(idemoments.Mco(is,:)')
|
|
% end
|
|
% set(gca,'ylim',[0 1])
|
|
% set(gca,'xticklabel','')
|
|
% for ip=1:nparam,
|
|
% text(ip,-0.02,name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
|
% end
|
|
% title('Multicollinearity in the moments')
|
|
% saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_moments'])
|
|
% eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_moments']);
|
|
% eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_moments']);
|
|
% if options_.nograph, close(gcf); end
|
|
|
|
if SampleSize==1 && advanced,
|
|
% identificaton patterns
|
|
for j=1:size(cosnJ,2),
|
|
fprintf('\n\n')
|
|
disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)'])
|
|
fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn')
|
|
for i=1:nparam,
|
|
namx='';
|
|
for in=1:j,
|
|
namx=[namx ' ' sprintf('%-15s',name{pars{i,j}(in)})];
|
|
end
|
|
fprintf('%-15s [%s] %10.3f\n',name{i},namx,cosnJ(i,j))
|
|
end
|
|
end
|
|
disp('')
|
|
[U,S,V]=svd(siJ./normJ(:,ones(nparam,1)),0);
|
|
if nparam<5,
|
|
f1 = figure('name','Identification patterns (moments)');
|
|
else
|
|
f1 = figure('name','Identification patterns (moments): SMALLEST SV');
|
|
f2 = figure('name','Identification patterns (moments): HIGHEST SV');
|
|
end
|
|
for j=1:min(nparam,8),
|
|
if j<5,
|
|
figure(f1),
|
|
jj=j;
|
|
else
|
|
figure(f2),
|
|
jj=j-4;
|
|
end
|
|
subplot(4,1,jj),
|
|
if j<5
|
|
bar(abs(V(:,end-j+1))),
|
|
Stit = S(end-j+1,end-j+1);
|
|
% if j==4 || j==nparam,
|
|
% xlabel('SMALLEST singular values'),
|
|
% end,
|
|
else
|
|
bar(abs(V(:,j))),
|
|
Stit = S(j,j);
|
|
% if j==8 || j==nparam,
|
|
% xlabel('LARGEST singular values'),
|
|
% end,
|
|
end
|
|
set(gca,'xticklabel','')
|
|
if j==4 || j==nparam || j==8,
|
|
for ip=1:nparam,
|
|
text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
end
|
|
title(['Singular value ',num2str(Stit)])
|
|
end
|
|
figure(f1);
|
|
saveas(f1,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_1'])
|
|
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1']);
|
|
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1']);
|
|
if nparam>4,
|
|
figure(f2),
|
|
saveas(f2,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_2'])
|
|
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']);
|
|
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']);
|
|
end
|
|
end
|
|
|
|
if advanced,
|
|
if SampleSize>1
|
|
figure('Name','Condition Number'),
|
|
subplot(221)
|
|
hist(log10(idemodel.cond))
|
|
title('log10 of Condition number in the model')
|
|
subplot(222)
|
|
hist(log10(idemoments.cond))
|
|
title('log10 of Condition number in the moments')
|
|
subplot(223)
|
|
hist(log10(idelre.cond))
|
|
title('log10 of Condition number in the LRE model')
|
|
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_COND'])
|
|
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
|
|
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
|
|
if options_.nograph, close(gcf); end
|
|
end
|
|
|
|
|
|
pco = NaN(np,np);
|
|
for j=1:np,
|
|
if SampleSize>1
|
|
pco(j+1:end,j) = mean(abs(squeeze(idelre.Pco(j+1:end,j,:))'));
|
|
else
|
|
pco(j+1:end,j) = abs(idelre.Pco(j+1:end,j))';
|
|
end
|
|
end
|
|
figure('name','Pairwise correlations in the LRE model'),
|
|
imagesc(pco',[0 1]);
|
|
set(gca,'xticklabel','')
|
|
set(gca,'yticklabel','')
|
|
for ip=1:nparam,
|
|
text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none')
|
|
text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
colorbar;
|
|
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_LRE'])
|
|
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_LRE']);
|
|
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_LRE']);
|
|
if options_.nograph, close(gcf); end
|
|
|
|
pco = NaN(nparam,nparam);
|
|
for j=1:nparam,
|
|
if SampleSize>1
|
|
pco(j+1:end,j) = mean(abs(squeeze(idemodel.Pco(j+1:end,j,:))'));
|
|
else
|
|
pco(j+1:end,j) = abs(idemodel.Pco(j+1:end,j))';
|
|
end
|
|
end
|
|
figure('name','Pairwise correlations in the model'),
|
|
imagesc(pco',[0 1]);
|
|
set(gca,'xticklabel','')
|
|
set(gca,'yticklabel','')
|
|
for ip=1:nparam,
|
|
text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none')
|
|
text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
colorbar;
|
|
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_model'])
|
|
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_model']);
|
|
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_model']);
|
|
if options_.nograph, close(gcf); end
|
|
|
|
for j=1:nparam,
|
|
if SampleSize>1
|
|
pco(j+1:end,j) = mean(abs(squeeze(idemoments.Pco(j+1:end,j,:))'));
|
|
else
|
|
pco(j+1:end,j) = abs(idemoments.Pco(j+1:end,j))';
|
|
end
|
|
end
|
|
figure('name','Pairwise correlations in the moments'),
|
|
imagesc(pco',[0 1]);
|
|
set(gca,'xticklabel','')
|
|
set(gca,'yticklabel','')
|
|
for ip=1:nparam,
|
|
text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none')
|
|
text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none')
|
|
end
|
|
colorbar;
|
|
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_moments'])
|
|
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_moments']);
|
|
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_moments']);
|
|
if options_.nograph, close(gcf); end
|
|
end
|
|
|
|
|
|
if exist('OCTAVE_VERSION')
|
|
warning('on'),
|
|
else
|
|
warning on,
|
|
end
|
|
|
|
disp(' ')
|
|
disp(['==== Identification analysis completed ====' ]),
|
|
disp(' ')
|
|
disp(' ')
|