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function disp_identification(pdraws, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, name, options_ident)
% disp_identification(pdraws, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, name, options_ident)
% -------------------------------------------------------------------------
% This function displays all identification analysis to the command line
% =========================================================================
% INPUTS
% pdraws: [SampleSize by totparam_nbr] parameter draws
% ide_reducedform: [structure] Containing results from identification
% analysis based on the reduced-form solution (Ratto
% and Iskrev, 2011).
% ide_moments: [structure] Containing results from identification
% analysis based on moments (Iskrev, 2010).
% ide_spectrum: [structure] Containing results from identification
% analysis based on the spectrum (Qu and Tkachenko, 2012).
% ide_minimal: [structure] Containing results from identification
% analysis based on the minimal state space system
% (Komunjer and Ng, 2011).
% name: [totparam_nbr by 1] string cell of parameter names
% options_ident: [structure] identification options
% -------------------------------------------------------------------------
% OUTPUTS
% * all output is printed on the command line
% -------------------------------------------------------------------------
% This function is called by
% * dynare_identification.m
% =========================================================================
% Copyright (C) 2010-2019 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/>.
% =========================================================================
[SampleSize, totparam_nbr] = size(pdraws);
no_identification_reducedform = options_ident.no_identification_reducedform;
no_identification_moments = options_ident.no_identification_moments;
no_identification_spectrum = options_ident.no_identification_spectrum;
no_identification_minimal = options_ident.no_identification_minimal;
tol_rank = options_ident.tol_rank;
checks_via_subsets = options_ident.checks_via_subsets;
%% Display settings
disp([' ']),
fprintf('Note that differences in the criteria could be due to numerical settings,\n')
fprintf('numerical errors or the method used to find problematic parameter sets.\n')
fprintf('Settings:\n')
if options_ident.analytic_derivation_mode == 0
fprintf(' Derivation mode for Jacobians: Analytic using sylvester equations\n');
elseif options_ident.analytic_derivation_mode == 1
fprintf(' Derivation mode for Jacobians: Analytic using kronecker products\n');
elseif options_ident.analytic_derivation_mode < 0
fprintf(' Derivation mode for Jacobians: Numerical\n');
end
if checks_via_subsets
fprintf(' Method to find problematic parameters: Rank condition on all possible subsets\n');
else
fprintf(' Method to find problematic parameters: Nullspace and multicorrelation coefficients\n');
end
if options_ident.normalize_jacobians == 1
fprintf(' Normalize Jacobians: Yes\n');
else
fprintf(' Normalize Jacobians: No\n');
end
fprintf(' Tolerance level for rank computations: %s\n',num2str(options_ident.tol_rank));
fprintf(' Tolerance level for selecting nonzero columns: %.0d\n',options_ident.tol_deriv);
fprintf(' Tolerance level for selecting nonzero singular values: %.0d\n',options_ident.tol_sv);
%% Display problematic parameter sets for different criteria in a loop
for jide = 1:4
no_warning_message_display = 1;
%% Set output strings depending on test
if jide == 1
strTest = 'REDUCED-FORM'; strJacobian = 'Tau'; strMeaning = 'Jacobian of steady state and reduced-form solution matrices';
if ~no_identification_reducedform
noidentification = 0; ide = ide_reducedform;
if SampleSize == 1
Jacob = ide.dREDUCEDFORM;
end
else %skip test
noidentification = 1; no_warning_message_display = 0;
end
elseif jide == 2
strTest = 'MINIMAL SYSTEM (Komunjer and Ng, 2011)'; strJacobian = 'Deltabar'; strMeaning = 'Jacobian of steady state and minimal system';
if options_ident.order == 2
strMeaning = 'Jacobian of first-order minimal system and second-order accurate mean';
elseif options_ident.order == 3
strMeaning = 'Jacobian of first-order minimal system and third-order accurate mean';
end
if ~no_identification_minimal
noidentification = 0; ide = ide_minimal;
if SampleSize == 1
Jacob = ide.dMINIMAL;
end
else %skip test
noidentification = 1; no_warning_message_display = 0;
end
elseif jide == 3
strTest = 'SPECTRUM (Qu and Tkachenko, 2012)'; strJacobian = 'Gbar'; strMeaning = 'Jacobian of mean and spectrum';
if options_ident.order > 1
strTest = 'SPECTRUM (Mutschler, 2015)';
end
if ~no_identification_spectrum
noidentification = 0; ide = ide_spectrum;
if SampleSize == 1
Jacob = ide.dSPECTRUM;
end
else %skip test
noidentification = 1; no_warning_message_display = 0;
end
elseif jide == 4
strTest = 'MOMENTS (Iskrev, 2010)'; strJacobian = 'J'; strMeaning = 'Jacobian of first two moments';
if options_ident.order > 1
strTest = 'MOMENTS (Mutschler, 2015)'; strJacobian = 'Mbar';
end
if ~no_identification_moments
noidentification = 0; ide = ide_moments;
if SampleSize == 1
Jacob = ide.si_dMOMENTS;
end
else %skip test
noidentification = 1; no_warning_message_display = 0;
end
end
if ~noidentification
%% display problematic parameters computed by identifcation_checks.m
if ~checks_via_subsets
if any(ide.ino) || any(any(ide.ind0==0)) || any(any(ide.jweak_pair))
no_warning_message_display=0;
skipline()
disp([upper(strTest), ':'])
disp(' !!!WARNING!!!');
if SampleSize>1
disp([' The rank of ', strJacobian, ' (', strMeaning, ') is deficient for ', num2str(length(find(ide.ino))),' out of ',int2str(SampleSize),' MC runs!' ]),
else
disp([' The rank of ', strJacobian, ' (', strMeaning, ') is deficient!']),
end
skipline()
for j=1:totparam_nbr
if any(ide.ind0(:,j)==0)
pno = 100*length(find(ide.ind0(:,j)==0))/SampleSize;
if SampleSize>1
disp([' ',name{j},' is not identified for ',num2str(pno),'% of MC runs!' ])
else
disp([' ',name{j},' is not identified!' ])
end
end
end
npairs=size(ide.jweak_pair,2);
jmap_pair=dyn_unvech(1:npairs);
jstore=[];
for j=1:npairs
iweak = length(find(ide.jweak_pair(:,j)));
if iweak
[jx,jy]=find(jmap_pair==j);
jstore=[jstore jx(1) jy(1)];
if SampleSize > 1
disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear for ',num2str((iweak)/SampleSize*100),'% of MC runs!' ])
else
disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear!' ])
end
end
end
for j=1:totparam_nbr
iweak = length(find(ide.jweak(:,j)));
if iweak && ~ismember(j,jstore)
if SampleSize>1
disp([' ',name{j},' is collinear w.r.t. all other parameters for ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
else
disp([' ',name{j},' is collinear w.r.t. all other parameters!' ])
end
end
end
end
%% display problematic parameters computed by identification_checks_via_subsets
elseif checks_via_subsets
if ide.rank < size(Jacob,2)
no_warning_message_display = 0;
skipline()
disp([upper(strTest), ':'])
disp(' !!!WARNING!!!');
if SampleSize>1
disp([' The rank of ', strJacobian, ' (', strMeaning, ') is deficient for ', num2str(length(find(ide.ino))),' out of ',int2str(SampleSize),' MC runs!' ]),
else
disp([' The rank of ', strJacobian, ' (', strMeaning, ') is deficient!']),
end
if all(cellfun(@isempty,ide.problpars))
disp([' No problematic parameter combinations with maximum dimension ', num2str(size(ide.problpars,2)), ' were found. Increase max_dim_subsets_groups.']);
skipline()
else
disp([' Displaying problematic parameter combinations (with maximum dimension ', num2str(size(ide.problpars,2)), '):']);
skipline()
probparamset_nbr = 0;
for jset = 1:size(ide.problpars,2)
if isempty(ide.problpars{jset}) == 0
for jrow = 1:size(ide.problpars{jset},1)
for j = transpose(ide.problpars{jset}(jrow,:))
probparamset_nbr = probparamset_nbr + 1;
%pno = 100*length(find(ide.ind0(:,j)==0))/SampleSize;
problparnamestring = strjoin(eval(['[', sprintf('name(%d), ', j), ']']),',');
if SampleSize > 1
if length(j) == 1
disp([' ',problparnamestring,' is not identified for ',num2str(pno),'% of MC runs!' ])
else
disp([' [',problparnamestring,'] are collinear (with tol = ', num2str(tol_rank), ') for ',num2str((iweak)/SampleSize*100),'% of MC runs!' ])
end
else
if length(j) == 1
disp([' ',problparnamestring, ' is not identified!' ])
else
disp([' [',problparnamestring, '] are collinear!' ])
end
end
end
end
end
end
end
end
end
end
%% All parameters are identified
if no_warning_message_display
skipline()
disp([upper(strTest), ':']);
disp([' All parameters are identified in the ', strMeaning, ' (rank(', strJacobian, ') is full with tol = ', num2str(tol_rank), ').' ]),
end
end
%% Advanced identificaton patterns
if SampleSize==1 && options_ident.advanced
skipline()
for j=1:size(ide_moments.cosndMOMENTS,2)
pax=NaN(totparam_nbr,totparam_nbr);
fprintf('\n')
disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)'])
fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn')
for i=1:totparam_nbr
namx='';
for in=1:j
dumpindx = ide_moments.pars{i,j}(in);
if isnan(dumpindx)
namx=[namx ' ' sprintf('%-15s','--')];
else
namx=[namx ' ' sprintf('%-15s',name{dumpindx})];
pax(i,dumpindx)=ide_moments.cosndMOMENTS(i,j);
end
end
fprintf('%-15s [%s] %14.7f\n',name{i},namx,ide_moments.cosndMOMENTS(i,j))
end
end
end
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