341 lines
11 KiB
Matlab
341 lines
11 KiB
Matlab
function x0 = stab_map_(Nsam, fload, alpha2, prepSA, pprior, ilptau)
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%
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% function x0 = stab_map_(Nsam, fload, alpha2, prepSA, pprior)
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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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% M. Ratto, Global Sensitivity Analysis for Macroeconomic models
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% I. Mapping stability, MIMEO, 2005.
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%
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% INPUTS
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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 lptauSEQ,
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% stab_map_1, stab_map_2
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%
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% Copyright (C) 2005 Marco Ratto
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% THIS PROGRAM WAS WRITTEN FOR MATLAB BY
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% Marco Ratto,
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% Unit of Econometrics and Statistics AF
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% (http://www.jrc.cec.eu.int/uasa/),
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% IPSC, Joint Research Centre
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% The European Commission,
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% TP 361, 21020 ISPRA(VA), ITALY
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% marco.ratto@jrc.it
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%
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% ALL COPIES MUST BE PROVIDED FREE OF CHARGE AND MUST INCLUDE THIS COPYRIGHT
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% NOTICE.
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%
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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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dr_ = oo_.dr;
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if isfield(dr_,'ghx'),
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ys_ = oo_.dr.ys;
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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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fname_ = M_.fname;
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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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if nargin==0,
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Nsam=2000; %2^13; %256;
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end
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if nargin<2,
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fload=0;
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end
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if nargin<3,
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alpha2=0.3;
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end
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if nargin<4,
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prepSA=0;
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end
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if nargin<5,
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pprior=1;
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end
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if nargin<6,
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ilptau=1;
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end
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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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if fload==0 | nargin<2 | isempty(fload),
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if estim_params_.np<52 & ilptau,
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[lpmat] = lptauSEQ(Nsam,estim_params_.np);
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if estim_params_.np>30
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for j=1:estim_params_.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
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%[lpmat] = rand(Nsam,estim_params_.np);
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for j=1:estim_params_.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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if pprior,
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for j=1:nshock,
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lpmat0(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube
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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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for j=1:estim_params_.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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% 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:estim_params_.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 estim_params_.np>30 & estim_params_.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 ';']');
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d = chol(inv(hh));
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lp=randn(Nsam,nshock+estim_params_.np)*d+kron(ones(Nsam,1),xparam1');
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lpmat0=lp(:,1:nshock);
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lpmat=lp(:,nshock+1:end);
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end
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%
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h = waitbar(0,'Please wait...');
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istable=[1:Nsam];
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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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stoch_simul([]);
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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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T(:,:,j) = [dr_.ghx dr_.ghu];
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end
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if ~exist('nspred'),
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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 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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else
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egg(:,j)=ones(size(egg,1),1).*1.1;
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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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waitbar(j/Nsam,h,['MC iteration ',int2str(j),'/',int2str(Nsam)])
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end
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close(h)
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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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if pprior,
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if ~prepSA
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save([fname_,'_prior'],'lpmat','lpmat0','iunstable','istable','iindeterm','iwrong','egg','yys','nspred','nboth','nfwrd')
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else
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save([fname_,'_prior'],'lpmat','lpmat0','iunstable','istable','iindeterm','iwrong','egg','yys','T','nspred','nboth','nfwrd')
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end
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else
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if ~prepSA
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save([fname_,'_mc'],'lpmat','lpmat0','iunstable','istable','iindeterm','iwrong','egg','yys','nspred','nboth','nfwrd')
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else
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save([fname_,'_mc'],'lpmat','lpmat0','iunstable','istable','iindeterm','iwrong','egg','yys','T','nspred','nboth','nfwrd')
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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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load([fname_,'_prior'])
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else
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load([fname_,'_mc'])
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end
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Nsam = size(lpmat,1);
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end
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if prepSA & ~exist('T'),
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h = 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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for j=1:length(istable),
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M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(j),:)';
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stoch_simul([]);
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dr_ = oo_.dr;
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T(:,:,j) = [dr_.ghx dr_.ghu];
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if ~exist('nspred')
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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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ys_=real(dr_.ys);
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yys(:,j) = ys_;
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ys_=yys(:,1);
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waitbar(j/Nsam,h,['MC iteration ',int2str(j),'/',int2str(Nsam)])
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end
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close(h)
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if pprior
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save([fname_,'_prior'],'T','-append')
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else
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save([fname_,'_mc'],'T','-append')
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end
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end
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if pprior
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aname='prior_stab';
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auname='prior_unacceptable';
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asname='prior_stable';
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else
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aname='mc_stab';
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auname='mc_unacceptable';
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asname='mc_stable';
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end
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delete([fname_,'_',aname,'_*.*']);
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delete([fname_,'_',aname,'_SA_*.*']);
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delete([fname_,'_',asname,'_corr_*.*']);
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delete([fname_,'_',auname,'_corr_*.*']);
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if length(iunstable)>0 & length(iunstable)<Nsam,
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disp([num2str(length(istable)/Nsam*100),'\% of the prior support is stable.'])
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if ~isempty(iwrong),
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disp(['For ',num2str(length(iwrong)/Nsam*100),'\% of the prior support dynare could not find a solution.'])
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end
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if ~isempty(iindeterm),
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disp([num2str(length(iindeterm)/Nsam*100),'\% of the prior support gives indeterminacy.'])
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end
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% Blanchard Kahn
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proba = stab_map_1(lpmat, istable, iunstable, aname);
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disp(' ')
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disp(' ')
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disp('Starting bivariate analysis:')
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c0=corrcoef(lpmat(istable,:));
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c00=tril(c0,-1);
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stab_map_2(lpmat(istable,:),alpha2, asname);
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stab_map_2(lpmat(iunstable,:),alpha2, auname);
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x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock);
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x0 = [x0; lpmat(istable(1),:)'];
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if istable(end)~=Nsam
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M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(1),:)';
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stoch_simul([]);
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end
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else
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if length(iunstable)==0,
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disp('All parameter values in the specified ranges are stable!')
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x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock);
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x0 = [x0; lpmat(istable(1),:)'];
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else
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disp('All parameter values in the specified ranges are not acceptable!')
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x0=[];
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end
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end
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