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