dynare_identification.m

function [pdraws, TAU, GAM, LRE, gp, H, JJ] = dynare_identification(options_ident, pdraws0)
%function [pdraws, TAU, GAM, LRE, gp, H, JJ] = dynare_identification(options_ident, pdraws0)
%
% INPUTS
%    o options_ident    [structure] identification options
%    o pdraws0          [matrix] optional: matrix of MC sample of model params. 
%    
% OUTPUTS
%    o pdraws           [matrix] matrix of MC sample of model params used
%    o TAU,             [matrix] MC sample of entries in the model solution (stacked vertically)
%    o GAM,             [matrix] MC sample of entries in the moments (stacked vertically)
%    o LRE,             [matrix] MC sample of entries in LRE model (stacked vertically)
%    o gp,              [matrix] derivatives of the Jacobian (LRE model)
%    o H,               [matrix] derivatives of the model solution
%    o JJ               [matrix] derivatives of the  moments
%    
% SPECIAL REQUIREMENTS
%    None

% main 
%
% Copyright (C) 2010-2013 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 <http://www.gnu.org/licenses/>.

global M_ options_ oo_ bayestopt_ estim_params_

if isoctave
    warning('off'),
else
    warning off,
end

fname_ = M_.fname;
if ~isfield(M_,'dname'),
    M_.dname = M_.fname;
end
options_ident = set_default_option(options_ident,'gsa_sample_file',0);
options_ident = set_default_option(options_ident,'parameter_set','prior_mean');
options_ident = set_default_option(options_ident,'load_ident_files',0);
options_ident = set_default_option(options_ident,'useautocorr',0);
options_ident = set_default_option(options_ident,'ar',1);
options_ident = set_default_option(options_ident,'prior_mc',1);
options_ident = set_default_option(options_ident,'prior_range',0);
options_ident = set_default_option(options_ident,'periods',300);
options_ident = set_default_option(options_ident,'replic',100);
options_ident = set_default_option(options_ident,'advanced',0);
options_ident = set_default_option(options_ident,'normalize_jacobians',1);
options_ident = set_default_option(options_ident,'lik_init',1);
options_ident = set_default_option(options_ident,'analytic_derivation',1);
if isfield(options_ident,'nograph'),
    options_.nograph=options_ident.nograph;
end
if isfield(options_ident,'nodisplay'),
    options_.nodisplay=options_ident.nodisplay;
end
if isfield(options_ident,'graph_format'),
    options_.graph_format=options_ident.graph_format;
end

if options_ident.gsa_sample_file,
    GSAFolder = checkpath('gsa',M_.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==1,
    pdraws0=[];
end
external_sample=0;
if nargin==2 || ~isempty(pdraws0),
    options_ident.prior_mc=size(pdraws0,1);
    options_ident.load_ident_files = 0;
    external_sample=1;
end
if isempty(estim_params_),
    options_ident.prior_mc=1;
    options_ident.prior_range=0;
    prior_exist=0;
else
    prior_exist=1;
    parameters = options_ident.parameter_set;
end

% options_ident.load_ident_files=1;
iload = options_ident.load_ident_files;
%options_ident.advanced=1;
advanced = options_ident.advanced;
nlags = options_ident.ar;
periods = options_ident.periods;
replic = options_ident.replic;
useautocorr = options_ident.useautocorr;
options_.order=1;
options_.ar=nlags;
options_.prior_mc = options_ident.prior_mc;
options_.options_ident = options_ident;
options_.Schur_vec_tol = 1.e-8;
options_.nomoments=0;
options_.analytic_derivation=1;

options_ = set_default_option(options_,'datafile','');
options_.mode_compute = 0;
options_.plot_priors = 0;
options_.smoother=1;
[dataset_,xparam1,hh, M_, options_, oo_, estim_params_,bayestopt_]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
options_ident.analytic_derivation_mode = options_.analytic_derivation_mode;
if isempty(dataset_),
    dataset_.info.ntobs = periods;
    dataset_.info.nvobs = rows(options_.varobs);
    dataset_.info.varobs = options_.varobs;
    dataset_.rawdata = [];
    dataset_.missing.state = 0;
    for jdata=1:periods,
        temp1{jdata}=[1:dataset_.info.nvobs]';
    end
    dataset_.missing.aindex = temp1;
    dataset_.missing.vindex = [];
    dataset_.missing.number_of_observations = [];
    dataset_.missing.no_more_missing_observations = 1;
    dataset_.descriptive.mean = [];
    dataset_.data = [];

%     data_info.gend = periods;
%     data_info.data = [];
%     data_info.data_index = [];
%     data_info.number_of_observations = periods*size(options_.varobs,1);
%     data_info.no_more_missing_observations = 0;
%     data_info.missing_value = 0;
end

% results = prior_sampler(0,M_,bayestopt_,options_,oo_);

if prior_exist
    if any(bayestopt_.pshape > 0)
        if options_ident.prior_range
            prior_draw(1,1);
        else
            prior_draw(1);
        end
    else
        options_ident.prior_mc=1;
    end
end

SampleSize = options_ident.prior_mc;

if ~(exist('sylvester3','file')==2),

    dynareroot = strrep(which('dynare'),'dynare.m','');
    addpath([dynareroot 'gensylv'])
end

IdentifDirectoryName = CheckPath('identification',M_.dname);
if prior_exist,

    indx = [];
    if ~isempty(estim_params_.param_vals),
        indx = estim_params_.param_vals(:,1);
    end
    indexo=[];
    if ~isempty(estim_params_.var_exo)
        indexo = estim_params_.var_exo(:,1);
    end

    nparam = length(bayestopt_.name);
    np = estim_params_.np;
    name = bayestopt_.name;
    name_tex = char(M_.exo_names_tex(indexo,:),M_.param_names_tex(indx,:));

    offset = estim_params_.nvx;
    offset = offset + estim_params_.nvn;
    offset = offset + estim_params_.ncx;
    offset = offset + estim_params_.ncn;
else
    indx = [1:M_.param_nbr];
    indexo = [1:M_.exo_nbr];
    offset = M_.exo_nbr;
    np = M_.param_nbr;
    nparam = np+offset;
    name = [cellstr(M_.exo_names); cellstr(M_.param_names)];
    name_tex = [cellstr(M_.exo_names_tex); cellstr(M_.param_names_tex)];
end

skipline()
disp(['==== Identification analysis ====' ]),
skipline()
if nparam<2,
    options_ident.advanced=0;
    advanced = options_ident.advanced;
    disp('There is only one parameter to study for identitification.')
    disp('The advanced option is re-set to 0.')
    skipline()
end

options_ident = set_default_option(options_ident,'max_dim_cova_group',min([2,nparam-1]));
options_ident.max_dim_cova_group = min([options_ident.max_dim_cova_group,nparam-1]);


MaxNumberOfBytes=options_.MaxNumberOfBytes;
store_options_ident = options_ident;

if iload <=0,
    
    [I,J]=find(M_.lead_lag_incidence');
    if prior_exist,
%         if exist([fname_,'_mean.mat'],'file'),
% %             disp('Testing posterior mean')
%             load([fname_,'_mean'],'xparam1')
%             pmean = xparam1';
%             clear xparam1
%         end
%         if exist([fname_,'_mode.mat'],'file'),
% %             disp('Testing posterior mode')
%             load([fname_,'_mode'],'xparam1')
%             pmode = xparam1';
%             clear xparam1
%         end
        params = set_prior(estim_params_,M_,options_)';
        if all(bayestopt_.pshape == 0)
            parameters = 'ML_Starting_value';
            disp('Testing ML Starting value')
        else
        switch parameters
            case 'posterior_mode'
                disp('Testing posterior mode')
                params(1,:) = get_posterior_parameters('mode');
            case 'posterior_mean'
                disp('Testing posterior mean')
                params(1,:) = get_posterior_parameters('mean');
            case 'posterior_median'
                disp('Testing posterior median')
                params(1,:) = get_posterior_parameters('median');
            case 'prior_mode'
                disp('Testing prior mode')
                params(1,:) = bayestopt_.p5(:);
            case 'prior_mean'
                disp('Testing prior mean')
                params(1,:) = bayestopt_.p1;
            otherwise
                disp('The option parameter_set has to be equal to:')
                disp('                   ''posterior_mode'', ')
                disp('                   ''posterior_mean'', ')
                disp('                   ''posterior_median'', ')
                disp('                   ''prior_mode'' or')
                disp('                   ''prior_mean''.')
                error;
        end
        end
    else
        params = [sqrt(diag(M_.Sigma_e))', M_.params'];
        parameters = 'Current_params';
        disp('Testing current parameter values')
    end
    [idehess_point, idemoments_point, idemodel_point, idelre_point, derivatives_info_point, info] = ...
        identification_analysis(params,indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
    if info(1)~=0,
        skipline()
        disp('----------- ')
        disp('Parameter error:')
        disp(['The model does not solve for ', parameters, ' with error code info = ', int2str(info(1))]),
        skipline()
        if info(1)==1,
        disp('info==1 %! The model doesn''t determine the current variables uniquely.')
        elseif info(1)==2,
        disp('info==2 %! MJDGGES returned an error code.')
        elseif info(1)==3,
        disp('info==3 %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium. ')
        elseif info(1)==4,
        disp('info==4 %! Blanchard & Kahn conditions are not satisfied: indeterminacy. ')
        elseif info(1)==5,
        disp('info==5 %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure. ')
        elseif info(1)==6,
        disp('info==6 %! The jacobian evaluated at the deterministic steady state is complex.')
        elseif info(1)==19,
        disp('info==19 %! The steadystate routine thrown an exception (inconsistent deep parameters). ')
        elseif info(1)==20,
        disp('info==20 %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations). ')
        elseif info(1)==21,
        disp('info==21 %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.')
        elseif info(1)==22,
        disp('info==22 %! The steady has NaNs. ')
        elseif info(1)==23,
        disp('info==23 %! M_.params has been updated in the steadystate routine and has complex valued scalars. ')
        elseif info(1)==24,
        disp('info==24 %! M_.params has been updated in the steadystate routine and has some NaNs. ')
        elseif info(1)==30,
        disp('info==30 %! Ergodic variance can''t be computed. ')
        end
        disp('----------- ')
        skipline()
        if any(bayestopt_.pshape)
            disp('Try sampling up to 50 parameter sets from the prior.')
            kk=0;
            while kk<50 && info(1),
                kk=kk+1;
                params = prior_draw();
                [idehess_point, idemoments_point, idemodel_point, idelre_point, derivatives_info_point, info] = ...
                    identification_analysis(params,indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
            end
        end
        if info(1)
            skipline()
            disp('----------- ')
            disp('Identification stopped:')
            if any(bayestopt_.pshape)
                disp('The model did not solve for any of 50 attempts of random samples from the prior')
            end
            disp('----------- ')
            skipline()
            return
        end
    else
    idehess_point.params=params;
%     siH = idemodel_point.siH;
%     siJ = idemoments_point.siJ;
%     siLRE = idelre_point.siLRE;
%     normH = max(abs(siH)')';
%     normJ = max(abs(siJ)')';
%     normLRE = max(abs(siLRE)')';
    save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_point', 'idemoments_point','idemodel_point', 'idelre_point','store_options_ident')
    disp_identification(params, idemodel_point, idemoments_point, name, advanced);
    if ~options_.nograph,
        plot_identification(params,idemoments_point,idehess_point,idemodel_point,idelre_point,advanced,parameters,name,IdentifDirectoryName);
    end
    end

    if SampleSize > 1,
        skipline()
        disp('Monte Carlo Testing')
        h = dyn_waitbar(0,'Monte Carlo identification checks ...');
        iteration = 0;
        loop_indx = 0;
        file_index = 0;
        run_index = 0;
        options_MC=options_ident;
        options_MC.advanced=0;
    else
        iteration = 1;
        pdraws = [];
    end
    while iteration < SampleSize,
        loop_indx = loop_indx+1;
        if external_sample,
            params = pdraws0(iteration+1,:);
        else
            params = prior_draw();
        end
        [dum1, ideJ, ideH, ideGP, dum2 , info] = ...
            identification_analysis(params,indx,indexo,options_MC,dataset_, prior_exist, name_tex,0);
        if iteration==0 && info(1)==0,
            MAX_tau   = min(SampleSize,ceil(MaxNumberOfBytes/(size(ideH.siH,1)*nparam)/8));
            stoH = zeros([size(ideH.siH,1),nparam,MAX_tau]);
            stoJJ = zeros([size(ideJ.siJ,1),nparam,MAX_tau]);
            stoLRE = zeros([size(ideGP.siLRE,1),np,MAX_tau]);
            TAU = zeros(size(ideH.siH,1),SampleSize);
            GAM = zeros(size(ideJ.siJ,1),SampleSize);
            LRE = zeros(size(ideGP.siLRE,1),SampleSize);
            pdraws = zeros(SampleSize,nparam);
            idemoments.indJJ = ideJ.indJJ;
            idemodel.indH = ideH.indH;
            idelre.indLRE = ideGP.indLRE;
            idemoments.ind0 = zeros(SampleSize,nparam);
            idemodel.ind0 = zeros(SampleSize,nparam);
            idelre.ind0 = zeros(SampleSize,np);
            idemoments.jweak = zeros(SampleSize,nparam);
            idemodel.jweak = zeros(SampleSize,nparam);
            idelre.jweak = zeros(SampleSize,np);
            idemoments.jweak_pair = zeros(SampleSize,nparam*(nparam+1)/2);
            idemodel.jweak_pair = zeros(SampleSize,nparam*(nparam+1)/2);
            idelre.jweak_pair = zeros(SampleSize,np*(np+1)/2);
            idemoments.cond = zeros(SampleSize,1);
            idemodel.cond = zeros(SampleSize,1);
            idelre.cond = zeros(SampleSize,1);
            idemoments.Mco = zeros(SampleSize,nparam);
            idemodel.Mco = zeros(SampleSize,nparam);
            idelre.Mco = zeros(SampleSize,np);
            idemoments.S = zeros(SampleSize,min(8,nparam));
            idemoments.V = zeros(SampleSize,nparam,min(8,nparam));
            delete([IdentifDirectoryName '/' M_.fname '_identif_*.mat'])
        end
        if info(1)==0,
            iteration = iteration + 1;
            run_index = run_index + 1;
            TAU(:,iteration)=ideH.TAU;
            LRE(:,iteration)=ideGP.LRE;
            GAM(:,iteration)=ideJ.GAM;
            idemoments.cond(iteration,1)=ideJ.cond;
            idemodel.cond(iteration,1)=ideH.cond;
            idelre.cond(iteration,1)=ideGP.cond;
            idemoments.ino(iteration,1)=ideJ.ino;
            idemodel.ino(iteration,1)=ideH.ino;
            idelre.ino(iteration,1)=ideGP.ino;
            idemoments.ind0(iteration,:)=ideJ.ind0;
            idemodel.ind0(iteration,:)=ideH.ind0;
            idelre.ind0(iteration,:)=ideGP.ind0;
            idemoments.jweak(iteration,:)=ideJ.jweak;
            idemodel.jweak(iteration,:)=ideH.jweak;
            idelre.jweak(iteration,:)=ideGP.jweak;
            idemoments.jweak_pair(iteration,:)=ideJ.jweak_pair;
            idemodel.jweak_pair(iteration,:)=ideH.jweak_pair;
            idelre.jweak_pair(iteration,:)=ideGP.jweak_pair;
            idemoments.Mco(iteration,:)=ideJ.Mco;
            idemodel.Mco(iteration,:)=ideH.Mco;
            idelre.Mco(iteration,:)=ideGP.Mco;
            idemoments.S(iteration,:)=ideJ.S;
            idemoments.V(iteration,:,:)=ideJ.V;
            stoLRE(:,:,run_index) = ideGP.siLRE;
            stoH(:,:,run_index) = ideH.siH;
            stoJJ(:,:,run_index) = ideJ.siJ;
            pdraws(iteration,:) = params;
            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;
                stoH = zeros(size(stoH));
                stoJJ = zeros(size(stoJJ));
                stoLRE = zeros(size(stoLRE));
                
            end
            
            if SampleSize > 1,
%                 if isoctave || 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 isoctave || 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 isoctave
    warning('on'),
else
    warning on,
end

skipline()
disp(['==== Identification analysis completed ====' ]),
skipline(2)