647 lines
26 KiB
Matlab
647 lines
26 KiB
Matlab
function x0 = stab_map_(OutputDirectoryName,opt_gsa)
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%
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% function x0 = stab_map_(OutputDirectoryName)
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%
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% Mapping of stability regions in the prior ranges applying
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% Monte Carlo filtering techniques.
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%
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% INPUTS (from opt_gsa structure)
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% Nsam = MC sample size
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% fload = 0 to run new MC; 1 to load prevoiusly generated analysis
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% alpha2 = significance level for bivariate sensitivity analysis
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% [abs(corrcoef) > alpha2]
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% prepSA = 1: save transition matrices for mapping reduced form
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% = 0: no transition matrix saved (default)
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% pprior = 1: sample from prior ranges (default): sample saved in
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% _prior.mat file
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% = 0: sample from posterior ranges: sample saved in
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% _mc.mat file
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% OUTPUT:
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% x0: one parameter vector for which the model is stable.
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%
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% GRAPHS
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% 1) Pdf's of marginal distributions under the stability (dotted
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% lines) and unstability (solid lines) regions
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% 2) Cumulative distributions of:
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% - stable subset (dotted lines)
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% - unacceptable subset (solid lines)
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% 3) Bivariate plots of significant correlation patterns
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% ( abs(corrcoef) > alpha2) under the stable and unacceptable subsets
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%
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% USES qmc_sequence, stab_map_1, stab_map_2
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%
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% Written by Marco Ratto
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% Joint Research Centre, The European Commission,
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% (http://eemc.jrc.ec.europa.eu/),
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% marco.ratto@jrc.it
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%
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% Reference:
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% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
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% Copyright (C) 2012-2013 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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%global bayestopt_ estim_params_ dr_ options_ ys_ fname_
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global bayestopt_ estim_params_ options_ oo_ M_
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% opt_gsa=options_.opt_gsa;
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Nsam = opt_gsa.Nsam;
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fload = opt_gsa.load_stab;
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ksstat = opt_gsa.ksstat;
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alpha2 = opt_gsa.alpha2_stab;
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pvalue_ks = opt_gsa.pvalue_ks;
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pvalue_corr = opt_gsa.pvalue_corr;
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prepSA = (opt_gsa.redform | opt_gsa.identification);
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pprior = opt_gsa.pprior;
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neighborhood_width = opt_gsa.neighborhood_width;
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ilptau = opt_gsa.ilptau;
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nliv = opt_gsa.morris_nliv;
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ntra = opt_gsa.morris_ntra;
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dr_ = oo_.dr;
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%if isfield(dr_,'ghx'),
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ys_ = oo_.dr.ys;
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nspred = M_.nspred; %size(dr_.ghx,2);
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nboth = M_.nboth;
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nfwrd = M_.nfwrd;
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%end
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fname_ = M_.fname;
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np = estim_params_.np;
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nshock = estim_params_.nvx;
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nshock = nshock + estim_params_.nvn;
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nshock = nshock + estim_params_.ncx;
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nshock = nshock + estim_params_.ncn;
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lpmat0=[];
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xparam1=[];
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pshape = bayestopt_.pshape(nshock+1:end);
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p1 = bayestopt_.p1(nshock+1:end);
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p2 = bayestopt_.p2(nshock+1:end);
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p3 = bayestopt_.p3(nshock+1:end);
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p4 = bayestopt_.p4(nshock+1:end);
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if nargin==0,
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OutputDirectoryName='';
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end
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opt=options_;
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options_.periods=0;
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options_.nomoments=1;
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options_.irf=0;
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options_.noprint=1;
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options_.simul=0;
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if fload==0,
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% if prepSA
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% T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),Nsam/2);
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% end
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if isfield(dr_,'ghx'),
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egg=zeros(length(dr_.eigval),Nsam);
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end
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yys=zeros(length(dr_.ys),Nsam);
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if opt_gsa.morris == 1
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[lpmat, OutFact] = Sampling_Function_2(nliv, np+nshock, ntra, ones(np+nshock, 1), zeros(np+nshock,1), []);
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lpmat = lpmat.*(nliv-1)/nliv+1/nliv/2;
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Nsam=size(lpmat,1);
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lpmat0 = lpmat(:,1:nshock);
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lpmat = lpmat(:,nshock+1:end);
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% elseif opt_gsa.morris==3,
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% lpmat = prep_ide(Nsam,np,5);
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% Nsam=size(lpmat,1);
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else
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if np<52 && ilptau>0,
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[lpmat] = qmc_sequence(np, int64(1), 0, Nsam)';
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if np>30 || ilptau==2, % scrambled lptau
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for j=1:np,
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lpmat(:,j)=lpmat(randperm(Nsam),j);
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end
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end
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else %ilptau==0
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%[lpmat] = rand(Nsam,np);
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for j=1:np,
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lpmat(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube
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end
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end
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end
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% try
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dummy=prior_draw_gsa(1);
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% catch
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% if pprior,
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% if opt_gsa.prior_range==0;
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% error('Some unknown prior is specified or ML estimation,: use prior_range=1 option!!');
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% end
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% end
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%
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% end
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if pprior,
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for j=1:nshock,
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if opt_gsa.morris~=1,
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lpmat0(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube
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end
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if opt_gsa.prior_range
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lpmat0(:,j)=lpmat0(:,j).*(bayestopt_.ub(j)-bayestopt_.lb(j))+bayestopt_.lb(j);
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end
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end
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if opt_gsa.prior_range
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% if opt_gsa.identification,
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% deltx=min(0.001, 1/Nsam/2);
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% for j=1:np,
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% xdelt(:,:,j)=prior_draw_gsa(0,[lpmat0 lpmat]+deltx);
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% end
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% end
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for j=1:np,
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lpmat(:,j)=lpmat(:,j).*(bayestopt_.ub(j+nshock)-bayestopt_.lb(j+nshock))+bayestopt_.lb(j+nshock);
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end
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else
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xx=prior_draw_gsa(0,[lpmat0 lpmat]);
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% if opt_gsa.identification,
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% deltx=min(0.001, 1/Nsam/2);
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% ldum=[lpmat0 lpmat];
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% ldum = prior_draw_gsa(0,ldum+deltx);
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% for j=1:nshock+np,
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% xdelt(:,:,j)=xx;
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% xdelt(:,j,j)=ldum(:,j);
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% end
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% clear ldum
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% end
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lpmat0=xx(:,1:nshock);
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lpmat=xx(:,nshock+1:end);
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clear xx;
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end
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else
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% for j=1:nshock,
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% xparam1(j) = oo_.posterior_mode.shocks_std.(bayestopt_.name{j});
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% sd(j) = oo_.posterior_std.shocks_std.(bayestopt_.name{j});
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% lpmat0(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube
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% lb = max(bayestopt_.lb(j), xparam1(j)-2*sd(j));
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% ub1=xparam1(j)+(xparam1(j) - lb); % define symmetric range around the mode!
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% ub = min(bayestopt_.ub(j),ub1);
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% if ub<ub1,
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% lb=xparam1(j)-(ub-xparam1(j)); % define symmetric range around the mode!
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% end
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% lpmat0(:,j) = lpmat0(:,j).*(ub-lb)+lb;
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% end
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% %
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% for j=1:np,
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% xparam1(j+nshock) = oo_.posterior_mode.parameters.(bayestopt_.name{j+nshock});
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% sd(j+nshock) = oo_.posterior_std.parameters.(bayestopt_.name{j+nshock});
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% lb = max(bayestopt_.lb(j+nshock),xparam1(j+nshock)-2*sd(j+nshock));
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% ub1=xparam1(j+nshock)+(xparam1(j+nshock) - lb); % define symmetric range around the mode!
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% ub = min(bayestopt_.ub(j+nshock),ub1);
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% if ub<ub1,
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% lb=xparam1(j+nshock)-(ub-xparam1(j+nshock)); % define symmetric range around the mode!
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% end
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% %ub = min(bayestopt_.ub(j+nshock),xparam1(j+nshock)+2*sd(j+nshock));
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% if np>30 & np<52
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% lpmat(:,j) = lpmat(randperm(Nsam),j).*(ub-lb)+lb;
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% else
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% lpmat(:,j) = lpmat(:,j).*(ub-lb)+lb;
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% end
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% end
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%load([fname_,'_mode'])
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eval(['load ' options_.mode_file '.mat;']);
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if neighborhood_width>0,
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for j=1:nshock,
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lpmat0(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube
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ub=min([bayestopt_.ub(j) xparam1(j)*(1+neighborhood_width)]);
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lb=max([bayestopt_.lb(j) xparam1(j)*(1-neighborhood_width)]);
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lpmat0(:,j)=lpmat0(:,j).*(ub-lb)+lb;
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end
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for j=1:np,
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ub=min([bayestopt_.ub(j+nshock) xparam1(j+nshock)*(1+neighborhood_width)]);
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lb=max([bayestopt_.lb(j+nshock) xparam1(j+nshock)*(1-neighborhood_width)]);
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lpmat(:,j)=lpmat(:,j).*(ub-lb)+lb;
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end
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else
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d = chol(inv(hh));
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lp=randn(Nsam*2,nshock+np)*d+kron(ones(Nsam*2,1),xparam1');
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for j=1:Nsam*2,
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lnprior(j) = any(lp(j,:)'<=bayestopt_.lb | lp(j,:)'>=bayestopt_.ub);
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end
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ireal=[1:2*Nsam];
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ireal=ireal(find(lnprior==0));
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lp=lp(ireal,:);
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Nsam=min(Nsam, length(ireal));
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lpmat0=lp(1:Nsam,1:nshock);
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lpmat=lp(1:Nsam,nshock+1:end);
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clear lp lnprior ireal;
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end
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end
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%
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h = dyn_waitbar(0,'Please wait...');
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istable=[1:Nsam];
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jstab=0;
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iunstable=[1:Nsam];
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iindeterm=zeros(1,Nsam);
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iwrong=zeros(1,Nsam);
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for j=1:Nsam,
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M_.params(estim_params_.param_vals(:,1)) = lpmat(j,:)';
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%try stoch_simul([]);
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try
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[Tt,Rr,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_,'restrict');
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infox(j,1)=info(1);
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if infox(j,1)==0 && ~exist('T'),
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dr_=oo_.dr;
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if prepSA,
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try
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T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),Nsam);
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catch ME
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if strcmp('MATLAB:nomem',ME.identifier),
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prepSA=0;
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disp('The model is too large for storing state space matrices ...')
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disp('for mapping reduced form or for identification')
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end
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T=[];
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end
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else
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T=[];
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end
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egg=zeros(length(dr_.eigval),Nsam);
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end
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if infox(j,1),
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% disp('no solution'),
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if isfield(oo_.dr,'ghx'),
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oo_.dr=rmfield(oo_.dr,'ghx');
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end
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if (infox(j,1)<3 || infox(j,1)>5) && isfield(oo_.dr,'eigval'),
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oo_.dr=rmfield(oo_.dr,'eigval');
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end
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end
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catch ME
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if isfield(oo_.dr,'eigval'),
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oo_.dr=rmfield(oo_.dr,'eigval');
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end
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if isfield(oo_.dr,'ghx'),
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oo_.dr=rmfield(oo_.dr,'ghx');
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end
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disp('No solution could be found'),
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end
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dr_ = oo_.dr;
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if isfield(dr_,'ghx'),
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egg(:,j) = sort(dr_.eigval);
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iunstable(j)=0;
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if prepSA
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jstab=jstab+1;
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T(:,:,jstab) = [dr_.ghx dr_.ghu];
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% [A,B] = ghx2transition(squeeze(T(:,:,jstab)), ...
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% bayestopt_.restrict_var_list, ...
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% bayestopt_.restrict_columns, ...
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% bayestopt_.restrict_aux);
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end
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if ~exist('nspred'),
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nspred = dr_.nspred; %size(dr_.ghx,2);
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nboth = dr_.nboth;
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nfwrd = dr_.nfwrd;
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end
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else
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istable(j)=0;
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if isfield(dr_,'eigval')
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egg(:,j) = sort(dr_.eigval);
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if exist('nspred')
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if any(isnan(egg(1:nspred,j)))
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iwrong(j)=j;
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else
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if (nboth || nfwrd) && abs(egg(nspred+1,j))<=options_.qz_criterium,
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iindeterm(j)=j;
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end
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end
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end
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else
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if exist('egg'),
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egg(:,j)=ones(size(egg,1),1).*NaN;
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end
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iwrong(j)=j;
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end
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end
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ys_=real(dr_.ys);
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yys(:,j) = ys_;
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ys_=yys(:,1);
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dyn_waitbar(j/Nsam,h,['MC iteration ',int2str(j),'/',int2str(Nsam)])
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end
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dyn_waitbar_close(h);
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if prepSA && jstab,
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T=T(:,:,1:jstab);
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else
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T=[];
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end
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istable=istable(find(istable)); % stable params
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iunstable=iunstable(find(iunstable)); % unstable params
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iindeterm=iindeterm(find(iindeterm)); % indeterminacy
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iwrong=iwrong(find(iwrong)); % dynare could not find solution
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% % map stable samples
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% istable=[1:Nsam];
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% for j=1:Nsam,
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% if any(isnan(egg(1:nspred,j)))
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% istable(j)=0;
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% else
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% if abs(egg(nspred,j))>=options_.qz_criterium; %(1-(options_.qz_criterium-1)); %1-1.e-5;
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% istable(j)=0;
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% %elseif (dr_.nboth | dr_.nfwrd) & abs(egg(nspred+1,j))<=options_.qz_criterium; %1+1.e-5;
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% elseif (nboth | nfwrd) & abs(egg(nspred+1,j))<=options_.qz_criterium; %1+1.e-5;
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% istable(j)=0;
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% end
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% end
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% end
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% istable=istable(find(istable)); % stable params
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%
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% % map unstable samples
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% iunstable=[1:Nsam];
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% for j=1:Nsam,
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% %if abs(egg(dr_.npred+1,j))>1+1.e-5 & abs(egg(dr_.npred,j))<1-1.e-5;
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% %if (dr_.nboth | dr_.nfwrd),
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% if ~any(isnan(egg(1:5,j)))
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% if (nboth | nfwrd),
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% if abs(egg(nspred+1,j))>options_.qz_criterium & abs(egg(nspred,j))<options_.qz_criterium; %(1-(options_.qz_criterium-1));
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% iunstable(j)=0;
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% end
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% else
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% if abs(egg(nspred,j))<options_.qz_criterium; %(1-(options_.qz_criterium-1));
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% iunstable(j)=0;
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% end
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% end
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% end
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% end
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% iunstable=iunstable(find(iunstable)); % unstable params
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bkpprior.pshape=bayestopt_.pshape;
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bkpprior.p1=bayestopt_.p1;
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bkpprior.p2=bayestopt_.p2;
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bkpprior.p3=bayestopt_.p3;
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bkpprior.p4=bayestopt_.p4;
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if pprior,
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if ~prepSA
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save([OutputDirectoryName filesep fname_ '_prior.mat'], ...
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'bkpprior','lpmat','lpmat0','iunstable','istable','iindeterm','iwrong', ...
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'egg','yys','nspred','nboth','nfwrd','infox')
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else
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save([OutputDirectoryName filesep fname_ '_prior.mat'], ...
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'bkpprior','lpmat','lpmat0','iunstable','istable','iindeterm','iwrong', ...
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'egg','yys','T','nspred','nboth','nfwrd','infox')
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end
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else
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if ~prepSA
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save([OutputDirectoryName filesep fname_ '_mc.mat'], ...
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'lpmat','lpmat0','iunstable','istable','iindeterm','iwrong', ...
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'egg','yys','nspred','nboth','nfwrd','infox')
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else
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save([OutputDirectoryName filesep fname_ '_mc.mat'], ...
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'lpmat','lpmat0','iunstable','istable','iindeterm','iwrong', ...
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'egg','yys','T','nspred','nboth','nfwrd','infox')
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end
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end
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else
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if pprior,
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filetoload=[OutputDirectoryName filesep fname_ '_prior.mat'];
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else
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filetoload=[OutputDirectoryName filesep fname_ '_mc.mat'];
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end
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load(filetoload,'lpmat','lpmat0','iunstable','istable','iindeterm','iwrong','egg','yys','nspred','nboth','nfwrd','infox')
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Nsam = size(lpmat,1);
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if pprior==0,
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eval(['load ' options_.mode_file '.mat;']);
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end
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if prepSA && isempty(strmatch('T',who('-file', filetoload),'exact')),
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h = dyn_waitbar(0,'Please wait...');
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options_.periods=0;
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options_.nomoments=1;
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options_.irf=0;
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options_.noprint=1;
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stoch_simul([]);
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%T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),length(istable));
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ntrans=length(istable);
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for j=1:ntrans,
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|
M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(j),:)';
|
|
%stoch_simul([]);
|
|
[Tt,Rr,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_,'restrict');
|
|
% This syntax is not compatible with the current version of dynare_resolve [stepan].
|
|
%[Tt,Rr,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
|
|
% bayestopt_.restrict_columns,...
|
|
% bayestopt_.restrict_aux);
|
|
if ~exist('T')
|
|
T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),ntrans);
|
|
end
|
|
dr_ = oo_.dr;
|
|
T(:,:,j) = [dr_.ghx dr_.ghu];
|
|
if ~exist('nspred')
|
|
nspred = dr_.nspred; %size(dr_.ghx,2);
|
|
nboth = dr_.nboth;
|
|
nfwrd = dr_.nfwrd;
|
|
end
|
|
ys_=real(dr_.ys);
|
|
yys(:,j) = ys_;
|
|
ys_=yys(:,1);
|
|
dyn_waitbar(j/ntrans,h,['MC iteration ',int2str(j),'/',int2str(ntrans)])
|
|
end
|
|
dyn_waitbar_close(h);
|
|
save(filetoload,'T','-append')
|
|
elseif prepSA
|
|
load(filetoload,'T')
|
|
end
|
|
end
|
|
|
|
if pprior
|
|
% univariate
|
|
aname='prior_stab'; atitle='Prior StabMap: Parameter driving non-existence of unique stable solution (Unacceptable)';
|
|
aindetname=[aname, '_indet']; aindettitle='Prior StabMap: Parameter driving indeterminacy';
|
|
aunstablename=[aname, '_unst']; aunstabletitle='Prior StabMap: Parameter driving explosiveness of solution';
|
|
awronguniname=[aname, '_wrong']; awrongunititle='Prior StabMap: Parameter driving inability to find solution';
|
|
% bivariate
|
|
auname='prior_unacceptable'; autitle='Prior Unacceptable';
|
|
aunstname='prior_unstable'; aunsttitle='Prior Unstable';
|
|
aindname='prior_indeterm'; aindtitle='Prior Indeterminacy';
|
|
awrongname='prior_wrong'; awrongtitle='Prior No Solution Found';
|
|
asname='prior_stable'; astitle='Prior Stable';
|
|
else
|
|
% univariate
|
|
aname='mc_stab'; atitle='Posterior StabMap: Parameter driving non-existence of unique stable solution (Unacceptable)';
|
|
aindetname=[aname, '_indet']; aindettitle='Posterior StabMap: Parameter driving indeterminacy';
|
|
aunstablename=[aname, '_unst']; aunstabletitle='Posterior StabMap: Parameter driving explosiveness of solution';
|
|
awronguniname=[aname, '_wrong']; awrongunititle='Posterior StabMap: Parameter driving inability to find solution';
|
|
% bivariate
|
|
auname='mc_unacceptable'; autitle='Posterior Unacceptable';
|
|
aunstname='mc_unstable'; aunsttitle='Posterior Unstable';
|
|
aindname='mc_indeterm'; aindtitle='Posterior Indeterminacy';
|
|
awrongname='mc_wrong'; awrongtitle='Posterior No Solution Found';
|
|
asname='mc_stable'; astitle='Posterior Stable';
|
|
end
|
|
delete([OutputDirectoryName,filesep,fname_,'_',aname,'_*.*']);
|
|
%delete([OutputDirectoryName,filesep,fname_,'_',aname,'_SA_*.*']);
|
|
delete([OutputDirectoryName,filesep,fname_,'_',asname,'_corr_*.*']);
|
|
delete([OutputDirectoryName,filesep,fname_,'_',auname,'_corr_*.*']);
|
|
delete([OutputDirectoryName,filesep,fname_,'_',aunstname,'_corr_*.*']);
|
|
delete([OutputDirectoryName,filesep,fname_,'_',aindname,'_corr_*.*']);
|
|
|
|
if length(iunstable)>0 && length(iunstable)<Nsam,
|
|
fprintf(['%4.1f%% of the prior support gives unique saddle-path solution.\n'],length(istable)/Nsam*100)
|
|
fprintf(['%4.1f%% of the prior support gives explosive dynamics.\n'],(length(iunstable)-length(iwrong)-length(iindeterm) )/Nsam*100)
|
|
if ~isempty(iindeterm),
|
|
fprintf(['%4.1f%% of the prior support gives indeterminacy.'],length(iindeterm)/Nsam*100)
|
|
end
|
|
if ~isempty(iwrong),
|
|
skipline()
|
|
disp(['For ',num2str(length(iwrong)/Nsam*100,'%1.3f'),'\% of the prior support dynare could not find a solution.'])
|
|
skipline()
|
|
if any(infox==1),
|
|
disp([' For ',num2str(length(find(infox==1))/Nsam*100,'%1.3f'),'\% The model doesn''t determine the current variables uniquely.'])
|
|
end
|
|
if any(infox==2),
|
|
disp([' For ',num2str(length(find(infox==2))/Nsam*100,'%1.3f'),'\% MJDGGES returned an error code.'])
|
|
end
|
|
if any(infox==6),
|
|
disp([' For ',num2str(length(find(infox==6))/Nsam*100,'%1.3f'),'\% The jacobian evaluated at the deterministic steady state is complex.'])
|
|
end
|
|
if any(infox==19),
|
|
disp([' For ',num2str(length(find(infox==19))/Nsam*100,'%1.3f'),'\% The steadystate routine thrown an exception (inconsistent deep parameters).'])
|
|
end
|
|
if any(infox==20),
|
|
disp([' For ',num2str(length(find(infox==20))/Nsam*100,'%1.3f'),'\% Cannot find the steady state.'])
|
|
end
|
|
if any(infox==21),
|
|
disp([' For ',num2str(length(find(infox==21))/Nsam*100,'%1.3f'),'\% The steady state is complex.'])
|
|
end
|
|
if any(infox==22),
|
|
disp([' For ',num2str(length(find(infox==22))/Nsam*100,'%1.3f'),'\% The steady has NaNs.'])
|
|
end
|
|
if any(infox==23),
|
|
disp([' For ',num2str(length(find(infox==23))/Nsam*100,'%1.3f'),'\% M_.params has been updated in the steadystate routine and has complex valued scalars.'])
|
|
end
|
|
if any(infox==24),
|
|
disp([' For ',num2str(length(find(infox==24))/Nsam*100,'%1.3f'),'\% M_.params has been updated in the steadystate routine and has some NaNs.'])
|
|
end
|
|
if any(infox==30),
|
|
disp([' For ',num2str(length(find(infox==30))/Nsam*100,'%1.3f'),'\% Ergodic variance can''t be computed.'])
|
|
end
|
|
|
|
end
|
|
skipline()
|
|
% Blanchard Kahn
|
|
[proba, dproba] = stab_map_1(lpmat, istable, iunstable, aname,0);
|
|
% indstab=find(dproba>ksstat);
|
|
indstab=find(proba<pvalue_ks);
|
|
disp('Smirnov statistics in driving acceptable behaviour')
|
|
for j=1:length(indstab),
|
|
disp([M_.param_names(estim_params_.param_vals(indstab(j),1),:),' d-stat = ', num2str(dproba(indstab(j)),'%1.3f'),' p-value = ', num2str(proba(indstab(j)),'%1.3f')])
|
|
end
|
|
skipline()
|
|
if ~isempty(indstab)
|
|
stab_map_1(lpmat, istable, iunstable, aname, 1, indstab, OutputDirectoryName,[],atitle);
|
|
end
|
|
ixun=iunstable(find(~ismember(iunstable,[iindeterm,iwrong])));
|
|
if ~isempty(iindeterm),
|
|
[proba, dproba] = stab_map_1(lpmat, [1:Nsam], iindeterm, aindetname ,0);
|
|
% indindet=find(dproba>ksstat);
|
|
indindet=find(proba<pvalue_ks);
|
|
disp('Smirnov statistics in driving indeterminacy')
|
|
for j=1:length(indindet),
|
|
disp([M_.param_names(estim_params_.param_vals(indindet(j),1),:),' d-stat = ', num2str(dproba(indindet(j)),'%1.3f'),' p-value = ', num2str(proba(indindet(j)),'%1.3f')])
|
|
end
|
|
skipline()
|
|
if ~isempty(indindet)
|
|
stab_map_1(lpmat, [1:Nsam], iindeterm, aindetname, 1, indindet, OutputDirectoryName,[],aindettitle);
|
|
end
|
|
end
|
|
|
|
if ~isempty(ixun),
|
|
[proba, dproba] = stab_map_1(lpmat, [1:Nsam], ixun, aunstablename,0);
|
|
% indunst=find(dproba>ksstat);
|
|
indunst=find(proba<pvalue_ks);
|
|
disp('Smirnov statistics in driving instability')
|
|
for j=1:length(indunst),
|
|
disp([M_.param_names(estim_params_.param_vals(indunst(j),1),:),' d-stat = ', num2str(dproba(indunst(j)),'%1.3f'),' p-value = ', num2str(proba(indunst(j)),'%1.3f')])
|
|
end
|
|
skipline()
|
|
if ~isempty(indunst)
|
|
stab_map_1(lpmat, [1:Nsam], ixun, aunstablename, 1, indunst, OutputDirectoryName,[],aunstabletitle);
|
|
end
|
|
end
|
|
|
|
if ~isempty(iwrong),
|
|
[proba, dproba] = stab_map_1(lpmat, [1:Nsam], iwrong, awronguniname,0);
|
|
% indwrong=find(dproba>ksstat);
|
|
indwrong=find(proba<pvalue_ks);
|
|
disp('Smirnov statistics in driving no solution')
|
|
for j=1:length(indwrong),
|
|
disp([M_.param_names(estim_params_.param_vals(indwrong(j),1),:),' d-stat = ', num2str(dproba(indwrong(j)),'%1.3f'),' p-value = ', num2str(proba(indwrong(j)),'%1.3f')])
|
|
end
|
|
skipline()
|
|
if ~isempty(indwrong)
|
|
stab_map_1(lpmat, [1:Nsam], iwrong, awronguniname, 1, indwrong, OutputDirectoryName,[],awrongunititle);
|
|
end
|
|
end
|
|
|
|
skipline()
|
|
disp('Starting bivariate analysis:')
|
|
|
|
c0=corrcoef(lpmat(istable,:));
|
|
c00=tril(c0,-1);
|
|
|
|
stab_map_2(lpmat(istable,:),alpha2, pvalue_corr, asname, OutputDirectoryName,xparam1,astitle);
|
|
if length(iunstable)>10,
|
|
stab_map_2(lpmat(iunstable,:),alpha2, pvalue_corr, auname, OutputDirectoryName,xparam1,autitle);
|
|
end
|
|
if length(iindeterm)>10,
|
|
stab_map_2(lpmat(iindeterm,:),alpha2, pvalue_corr, aindname, OutputDirectoryName,xparam1,aindtitle);
|
|
end
|
|
if length(ixun)>10,
|
|
stab_map_2(lpmat(ixun,:),alpha2, pvalue_corr, aunstname, OutputDirectoryName,xparam1,aunsttitle);
|
|
end
|
|
if length(iwrong)>10,
|
|
stab_map_2(lpmat(iwrong,:),alpha2, pvalue_corr, awrongname, OutputDirectoryName,xparam1,awrongtitle);
|
|
end
|
|
|
|
x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock);
|
|
x0 = [x0; lpmat(istable(1),:)'];
|
|
if istable(end)~=Nsam
|
|
M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(1),:)';
|
|
[oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
|
|
% stoch_simul([]);
|
|
end
|
|
else
|
|
if length(iunstable)==0,
|
|
disp('All parameter values in the specified ranges give unique saddle-path solution!')
|
|
x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock);
|
|
x0 = [x0; lpmat(istable(1),:)'];
|
|
else
|
|
disp('All parameter values in the specified ranges are not acceptable!')
|
|
x0=[];
|
|
end
|
|
|
|
end
|
|
|
|
xparam1=x0;
|
|
save prior_ok xparam1;
|
|
|
|
options_.periods=opt.periods;
|
|
if isfield(opt,'nomoments'),
|
|
options_.nomoments=opt.nomoments;
|
|
end
|
|
options_.irf=opt.irf;
|
|
options_.noprint=opt.noprint;
|
|
if isfield(opt,'simul'),
|
|
options_.simul=opt.simul;
|
|
end
|
|
|
|
|
|
|