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Merge pull request #886 from rattoma/gsa 

Enhance and harmonize reduced form mapping routine
time-shift
Stéphane Adjemian 2015-04-17 08:41:32 +02:00
parent 8d3a10c952 b0f4f2af78
commit 0a27b71f7e
6 changed files with 464 additions and 105 deletions
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33
matlab/gsa/filt_mc_.m
View File

@ -242,18 +242,39 @@ end
if ~options_.opt_gsa.ppost && options_.opt_gsa.lik_only
if options_.opt_gsa.pprior
anam='rmse_prior_post';
atitle='RMSE prior: Log Posterior Kernel';
else
anam='rmse_mc_post';
atitle='RMSE MC: Log Posterior Kernel';
end
stab_map_1(x, ipost(1:nfilt), ipost(nfilt+1:end), anam, 1,[],OutDir);
stab_map_2(x(ipost(1:nfilt),:),alpha2,pvalue,anam, OutDir);
options_mcf.pvalue_ks = alpha;
options_mcf.pvalue_corr = pvalue;
options_mcf.alpha2 = alpha2;
options_mcf.param_names = char(bayestopt_.name);
options_mcf.fname_ = fname_;
options_mcf.OutputDirectoryName = OutDir;
options_mcf.amcf_name = anam;
options_mcf.amcf_title = atitle;
options_mcf.title = atitle;
options_mcf.beha_title = 'better posterior kernel';
options_mcf.nobeha_title = 'worse posterior kernel';
mcf_analysis(x, ipost(1:nfilt), ipost(nfilt+1:end), options_mcf, options_);
if options_.opt_gsa.pprior
anam='rmse_prior_lik';
anam = 'rmse_prior_lik';
atitle = 'RMSE prior: Log Likelihood Kernel';
else
anam='rmse_mc_lik';
atitle = 'RMSE MC: Log Likelihood Kernel';
end
stab_map_1(x, ilik(1:nfilt), ilik(nfilt+1:end), anam, 1,[],OutDir);
stab_map_2(x(ilik(1:nfilt),:),alpha2,pvalue,anam, OutDir);
options_mcf.amcf_name = anam;
options_mcf.amcf_title = atitle;
options_mcf.title = atitle;
options_mcf.beha_title = 'better likelihood';
options_mcf.nobeha_title = 'worse likelihood';
mcf_analysis(x, ilik(1:nfilt), ilik(nfilt+1:end), options_mcf, options_);
else
if options_.opt_gsa.ppost,
rmse_txt=rmse_pmean;
@ -588,7 +609,7 @@ else
options_mcf.beha_title = ['better fit of ' deblank(vvarvecm(iy,:))];
options_mcf.nobeha_title = ['worse fit of ' deblank(vvarvecm(iy,:))];
options_mcf.title = ['the fit of ' deblank(vvarvecm(iy,:))];
mcf_analysis(x, ixx(1:nfilt0(iy),iy), ixx(nfilt0(iy)+1:end,iy), options_mcf, options_)
mcf_analysis(x, ixx(1:nfilt0(iy),iy), ixx(nfilt0(iy)+1:end,iy), options_mcf, options_);
end
for iy=1:size(vvarvecm,1),
ipar = find(any(squeeze(PPV(iy,:,:))<alpha));

9
matlab/gsa/log_trans_.m
View File

@ -1,4 +1,4 @@
function [yy, xdir, isig, lam]=log_trans_(y0,xdir0)
function [yy, xdir, isig, lam]=log_trans_(y0,xdir0,isig,lam)
% Copyright (C) 2012 Dynare Team
%
@ -17,6 +17,12 @@ function [yy, xdir, isig, lam]=log_trans_(y0,xdir0)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin==4,
% inverse transformation
yy = (exp(y0)-lam)*isig;
return
end
if nargin==1,
xdir0='';
end
@ -67,5 +73,6 @@ else
lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
end
end
lam = max(lam,0);
yy = log(y0+lam);
end

2
matlab/gsa/mcf_analysis.m
View File

@ -1,4 +1,4 @@
function mcf_analysis(lpmat, ibeha, inobeha, options_mcf, DynareOptions)
function indmcf = mcf_analysis(lpmat, ibeha, inobeha, options_mcf, DynareOptions)
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,

492
matlab/gsa/redform_map.m
View File

@ -53,21 +53,31 @@ alpha2 = options_gsa_.alpha2_redform;
alpha2=0;
pvalue_ks = options_gsa_.ksstat_redform;
pvalue_corr = options_gsa_.alpha2_redform;
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
fname_ = M_.fname;
bounds = prior_bounds(bayestopt_,options_);
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
if nargin==0,
dirname='';
end
if pprior
load([dirname,filesep,M_.fname,'_prior'],'lpmat', 'lpmat0', 'istable','T');
adir=[dirname filesep 'redform_stab'];
adir=[dirname filesep 'redform_prior'];
type = 'prior';
else
load([dirname,filesep,M_.fname,'_mc'],'lpmat', 'lpmat0', 'istable','T');
adir=[dirname filesep 'redform_mc'];
type = 'mc';
end
options_mcf.pvalue_ks = options_gsa_.ksstat_redform;
options_mcf.pvalue_corr = options_gsa_.alpha2_redform;
options_mcf.alpha2 = options_gsa_.alpha2_redform;
options_mcf.param_names = pnames;
options_mcf.fname_ = M_.fname;
options_mcf.OutputDirectoryName = adir;
if ~exist('T')
stab_map_(dirname,options_gsa_);
if pprior
@ -106,6 +116,15 @@ else
pd = [bayestopt_.p6(offset+1:end) bayestopt_.p7(offset+1:end) bayestopt_.p3(offset+1:end) bayestopt_.p4(offset+1:end)];
end
options_map.param_names = pnames;
options_map.fname_ = M_.fname;
options_map.OutputDirectoryName = adir;
options_map.iload = iload;
options_map.log_trans = ilog;
options_map.prior_range = options_gsa_.prior_range;
options_map.pshape = pshape;
options_map.pd = pd;
nsok = length(find(M_.lead_lag_incidence(M_.maximum_lag,:)));
lpmat=[];
lpmat0=[];
@ -119,7 +138,7 @@ for j=1:size(anamendo,1)
namexo=deblank(anamexo(jx,:));
iexo=strmatch(namexo,M_.exo_names,'exact');
skipline()
disp(['[', namendo,' vs. ',namexo,']'])
disp(['[', namendo,' vs ',namexo,']'])
if ~isempty(iexo),
@ -128,7 +147,7 @@ for j=1:size(anamendo,1)
if (max(y0)-min(y0))>1.e-10,
if mod(iplo,9)==0 && isempty(threshold) && ~options_.nograph,
ifig=ifig+1;
hfig = dyn_figure(options_,'name',['Reduced Form Mapping: ', namendo,' vs. shocks ',int2str(ifig)]);
hfig = dyn_figure(options_,'name',['Reduced Form Mapping: ', namendo,' vs shocks ',int2str(ifig)]);
iplo=0;
end
iplo=iplo+1;
@ -139,7 +158,15 @@ for j=1:size(anamendo,1)
if isempty(dir(xdir0))
mkdir(xdir0)
end
si(:,js) = redform_private(x0, y0, pshape, pd, iload, pnames, namendo, namexo, xdir0, options_gsa_);
atitle0=['Reduced Form Mapping (ANOVA) for ',namendo,' vs ', namexo];
aname=[type '_' namendo '_vs_' namexo];
atitle=[type ' Reduced Form Mapping (ANOVA): Parameter(s) driving ',namendo,' vs ',namexo];
options_map.amap_name = aname;
options_map.amap_title = atitle;
options_map.figtitle = atitle0;
options_map.title = [namendo,' vs ', namexo];
options_map.OutputDirectoryName = xdir0;
si(:,js) = redform_private(x0, y0, options_map, options_);
else
iy=find( (y0>threshold(1)) & (y0<threshold(2)));
iyc=find( (y0<=threshold(1)) | (y0>=threshold(2)));
@ -148,41 +175,65 @@ for j=1:size(anamendo,1)
mkdir(xdir)
end
if ~options_.nograph,
hf=dyn_figure(options_,'name',['Reduced Form Mapping: ',namendo,' vs. ', namexo]); hist(y0,30), title([namendo,' vs. ', namexo],'interpreter','none')
dyn_saveas(hf,[xdir,filesep, namendo,'_vs_', namexo],options_);
hf=dyn_figure(options_,'name',['Reduced Form Mapping (Monte Carlo Filtering): ',namendo,' vs ', namexo]);
hc = cumplot(y0);
a=axis; delete(hc);
% hist(mat_moment{ij}),
x1val=max(threshold(1),a(1));
x2val=min(threshold(2),a(2));
hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'b');
set(hp,'FaceColor', [0.7 0.8 1])
hold all,
hc = cumplot(y0);
set(hc,'color','k','linewidth',2)
hold off,
title([namendo,' vs ', namexo ' - threshold [' num2str(threshold(1)) ' ' num2str(threshold(2)) ']'],'interpreter','none')
dyn_saveas(hf,[xdir,filesep, fname_ '_' type '_' namendo,'_vs_', namexo],options_);
end
% if ~isempty(iy),
% si(:,js) = redform_private(x0(iy,:), y0(iy), pshape, pd, iload, pnames, namendo, namexo, xdir, options_gsa_);
% else
si(:,js) = NaN(np,1);
% end
if length(iy)>size(x0,2) && length(iyc)>size(x0,2)
delete([xdir, '/*threshold*.*'])
[proba, dproba] = stab_map_1(x0, iy, iyc, 'threshold',0);
% indsmirnov = find(dproba>ksstat);
indsmirnov = find(proba<pvalue_ks);
for jp=1:length(indsmirnov),
disp([M_.param_names(estim_params_.param_vals(indsmirnov(jp),1),:),' d-stat = ', num2str(dproba(indsmirnov(jp)),'%1.3f'),' p-value = ', num2str(proba(indsmirnov(jp)),'%1.3f')])
end
skipline()
stab_map_1(x0, iy, iyc, 'threshold',pvalue_ks,indsmirnov,xdir,[],['Reduced Form Mapping (Threshold) for ', namendo,' vs. lagged ', namexo]);
stab_map_2(x0(iy,:),alpha2,pvalue_corr,'inside_threshold',xdir,[],['Reduced Form Mapping (Inside Threshold)for ', namendo,' vs. lagged ', namexo])
stab_map_2(x0(iyc,:),alpha2,pvalue_corr,'outside_threshold',xdir,[],['Reduced Form Mapping (Outside Threshold) for ', namendo,' vs. lagged ', namexo])
delete([xdir, '/*threshold*.*'])
atitle0=['Reduced Form Mapping (Monte Carlo Filtering) for ',namendo,' vs ', namexo];
aname=[type '_' namendo '_vs_' namexo '_threshold'];
atitle=[type ' Reduced Form Mapping (Monte Carlo Filtering): Parameter(s) driving ',namendo,' vs ',namexo];
options_mcf.amcf_name = aname;
options_mcf.amcf_title = atitle;
options_mcf.beha_title = 'inside threshold';
options_mcf.nobeha_title = 'outside threshold';
options_mcf.title = atitle0;
options_mcf.OutputDirectoryName = xdir;
if ~isempty(iy) && ~isempty(iyc)
fprintf(['%4.1f%% of the ',type,' support matches ',atitle0,'\n'],length(iy)/length(y0)*100)
icheck = mcf_analysis(x0, iy, iyc, options_mcf, options_);
lpmat=x0(iy,:);
if nshocks,
lpmat0=xx0(iy,:);
end
istable=[1:length(iy)];
save([xdir,filesep,'threshold.mat'],'lpmat','lpmat0','istable','y0','x0','xx0','iy','iyc')
save([xdir,filesep, fname_ '_' type '_' namendo,'_vs_', namexo '_threshold' ],'lpmat','lpmat0','istable','y0','x0','xx0','iy','iyc')
lpmat=[]; lpmat0=[]; istable=[];
else
icheck=[];
end
if isempty(icheck),
atitle0=['Monte Carlo Filtering for ',namendo,' vs ', namexo];
options_mcf.title = atitle0;
indmcf = redform_mcf(y0, x0, options_mcf, options_);
end
end
else
[yy, xdir] = log_trans_(y0,xdir0);
if isempty(dir(xdir))
mkdir(xdir)
end
silog(:,js) = redform_private(x0, yy, pshape, pd, iload, pnames, namendo, namexo, xdir, options_gsa_);
atitle0=['Reduced Form Mapping (ANOVA) for log-transformed ',namendo,' vs ', namexo];
aname=[type '_' namendo '_vs_' namexo];
atitle=[type ' Reduced Form Mapping (ANOVA): Parameter(s) driving ',namendo,' vs ',namexo];
options_map.amap_name = aname;
options_map.amap_title = atitle;
options_map.figtitle = atitle0;
options_map.title = ['log(' namendo ' vs ' namexo ')'];
options_map.OutputDirectoryName = xdir0;
silog(:,js) = redform_private(x0, y0, options_map, options_);
end
if isempty(threshold) && ~options_.nograph,
@ -203,7 +254,7 @@ for j=1:size(anamendo,1)
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([logflag,' ',namendo,' vs. ',namexo],'interpreter','none')
title([logflag,' ',namendo,' vs ',namexo],'interpreter','none')
if iplo==9,
dyn_saveas(hfig,[dirname,filesep,M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)],options_);
end
@ -221,7 +272,7 @@ for j=1:size(anamendo,1)
namlagendo=deblank(anamlagendo(je,:));
ilagendo=strmatch(namlagendo,M_.endo_names(oo_.dr.order_var(M_.nstatic+1:M_.nstatic+nsok),:),'exact');
skipline()
disp(['[', namendo,' vs. lagged ',namlagendo,']'])
disp(['[', namendo,' vs lagged ',namlagendo,']'])
if ~isempty(ilagendo),
%y0=squeeze(T(iendo,ilagendo,istable));
@ -229,7 +280,7 @@ for j=1:size(anamendo,1)
if (max(y0)-min(y0))>1.e-10,
if mod(iplo,9)==0 && isempty(threshold) && ~options_.nograph,
ifig=ifig+1;
hfig = dyn_figure(options_,'name',['Reduced Form Mapping: ' namendo,' vs. lags ',int2str(ifig)]);
hfig = dyn_figure(options_,'name',['Reduced Form Mapping: ' namendo,' vs lags ',int2str(ifig)]);
iplo=0;
end
iplo=iplo+1;
@ -240,7 +291,15 @@ for j=1:size(anamendo,1)
if isempty(dir(xdir0))
mkdir(xdir0)
end
si(:,js) = redform_private(x0, y0, pshape, pd, iload, pnames, namendo, namlagendo, xdir0, options_gsa_);
atitle0=['Reduced Form Mapping (ANOVA) for ',namendo,' vs ', namlagendo];
aname=[type '_' namendo '_vs_' namlagendo];
atitle=[type ' Reduced Form Mapping (ANOVA): Parameter(s) driving ',namendo,' vs ',namlagendo];
options_map.amap_name = aname;
options_map.amap_title = atitle;
options_map.figtitle = atitle0;
options_map.title = [namendo,' vs ', namlagendo];
options_map.OutputDirectoryName = xdir0;
si(:,js) = redform_private(x0, y0, options_map, options_);
else
iy=find( (y0>threshold(1)) & (y0<threshold(2)));
iyc=find( (y0<=threshold(1)) | (y0>=threshold(2)));
@ -248,41 +307,67 @@ for j=1:size(anamendo,1)
if isempty(dir(xdir))
mkdir(xdir)
end
% if ~isempty(iy)
% si(:,js) = redform_private(x0(iy,:), y0(iy), pshape, pd, iload, pnames, namendo, namlagendo, xdir, options_gsa_);
% end
if ~options_.nograph,
hf=dyn_figure(options_,'name',['Reduced Form Mapping: ',namendo,' vs. lagged ', namlagendo]); hist(y0,30), title([namendo,' vs. lagged ', namlagendo],'interpreter','none')
dyn_saveas(hf,[xdir,filesep, namendo,'_vs_', namlagendo],options_);
hf=dyn_figure(options_,'name',['Reduced Form Mapping (Monte Carlo Filtering): ',namendo,' vs lagged ', namlagendo]);
hc = cumplot(y0);
a=axis; delete(hc);
% hist(mat_moment{ij}),
x1val=max(threshold(1),a(1));
x2val=min(threshold(2),a(2));
hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'b');
set(hp,'FaceColor', [0.7 0.8 1])
hold all,
hc = cumplot(y0);
set(hc,'color','k','linewidth',2)
hold off,
title([namendo,' vs lagged ', namlagendo ' - threshold [' num2str(threshold(1)) ' ' num2str(threshold(2)) ']'],'interpreter','none')
dyn_saveas(hf,[xdir,filesep, fname_ '_' type '_' namendo,'_vs_', namlagendo],options_);
end
if length(iy)>size(x0,2) && length(iyc)>size(x0,2),
delete([xdir, '/*threshold*.*'])
[proba, dproba] = stab_map_1(x0, iy, iyc, 'threshold',0);
% indsmirnov = find(dproba>ksstat);
indsmirnov = find(proba<pvalue_ks);
for jp=1:length(indsmirnov),
disp([M_.param_names(estim_params_.param_vals(indsmirnov(jp),1),:),' d-stat = ', num2str(dproba(indsmirnov(jp)),'%1.3f'),' p-value = ', num2str(proba(indsmirnov(jp)),'%1.3f')])
end
skipline()
stab_map_1(x0, iy, iyc, 'threshold',pvalue_ks,indsmirnov,xdir,[],['Reduced Form Mapping (Threshold) for ', namendo,' vs. lagged ', namlagendo]);
stab_map_2(x0(iy,:),alpha2,pvalue_corr,'inside_threshold',xdir,[],['Reduced Form Mapping (Inside Threshold) for ', namendo,' vs. lagged ', namlagendo])
stab_map_2(x0(iyc,:),alpha2,pvalue_corr,'outside_threshold',xdir,[],['Reduced Form Mapping (Outside Threshold) for ', namendo,' vs. lagged ', namlagendo])
delete([xdir, '/*threshold*.*'])
atitle0=['Reduced Form Mapping (Monte Carlo Filtering) for ',namendo,' vs ', namlagendo];
aname=[type '_' namendo '_vs_' namlagendo '_threshold'];
atitle=[type ' Reduced Form Mapping (Monte Carlo Filtering): Parameter(s) driving ',namendo,' vs ',namlagendo];
options_mcf.amcf_name = aname;
options_mcf.amcf_title = atitle;
options_mcf.beha_title = 'inside threshold';
options_mcf.nobeha_title = 'outside threshold';
options_mcf.title = atitle0;
options_mcf.OutputDirectoryName = xdir;
if ~isempty(iy) && ~isempty(iyc)
fprintf(['%4.1f%% of the ',type,' support matches ',atitle0,'\n'],length(iy)/length(y0)*100)
icheck = mcf_analysis(x0, iy, iyc, options_mcf, options_);
lpmat=x0(iy,:);
if nshocks,
lpmat0=xx0(iy,:);
end
istable=[1:length(iy)];
save([xdir,filesep,'threshold.mat'],'lpmat','lpmat0','istable','y0','x0','xx0','iy','iyc')
save([xdir,filesep, fname_ '_' type '_' namendo,'_vs_', namlagendo '_threshold' ],'lpmat','lpmat0','istable','y0','x0','xx0','iy','iyc')
lpmat=[]; lpmat0=[]; istable=[];
else
icheck = [];
end
if isempty(icheck),
atitle0=['Monte Carlo Filtering for ',namendo,' vs ', namlagendo];
options_mcf.title = atitle0;
indmcf = redform_mcf(y0, x0, options_mcf, options_);
end
end
else
[yy, xdir] = log_trans_(y0,xdir0);
if isempty(dir(xdir))
mkdir(xdir)
end
silog(:,js) = redform_private(x0, yy, pshape, pd, iload, pnames, namendo, namlagendo, xdir, options_gsa_);
atitle0=['Reduced Form Mapping (ANOVA) for log-transformed ',namendo,' vs ', namlagendo];
aname=[type '_' namendo '_vs_' namlagendo];
atitle=[type ' Reduced Form Mapping (ANOVA): Parameter(s) driving ',namendo,' vs ',namlagendo];
options_map.amap_name = aname;
options_map.amap_title = atitle;
options_map.figtitle = atitle0;
options_map.title = ['log(' namendo ' vs ' namlagendo ')'];
options_map.OutputDirectoryName = xdir0;
silog(:,js) = redform_private(x0, y0, options_map, options_);
end
if isempty(threshold) && ~options_.nograph
@ -303,7 +388,7 @@ for j=1:size(anamendo,1)
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([logflag,' ',namendo,' vs. ',namlagendo,'(-1)'],'interpreter','none')
title([logflag,' ',namendo,' vs ',namlagendo,'(-1)'],'interpreter','none')
if iplo==9,
dyn_saveas(hfig,[dirname,filesep,M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)],options_);
end
@ -351,13 +436,17 @@ if isempty(threshold) && ~options_.nograph,
end
end
function si = redform_private(x0, y0, pshape, pd, iload, pnames, namy, namx, xdir, opt_gsa)
global bayestopt_ options_
function si = redform_private(x0, y0, options_map, options_)
% opt_gsa=options_.opt_gsa;
np=size(x0,2);
x00=x0;
if opt_gsa.prior_range,
ilog = options_map.log_trans;
iload = options_map.iload;
pnames = options_map.param_names;
pd = options_map.pd;
pshape = options_map.pshape;
xdir = options_map.OutputDirectoryName;
if options_map.prior_range,
for j=1:np,
x0(:,j)=(x0(:,j)-pd(j,3))./(pd(j,4)-pd(j,3));
end
@ -365,61 +454,286 @@ else
x0=priorcdf(x0,pshape, pd(:,1), pd(:,2), pd(:,3), pd(:,4));
end
fname=[xdir,'/map'];
if ilog,
fname=[xdir filesep options_map.fname_ '_' options_map.amap_name '_log'];
else
fname=[xdir filesep options_map.fname_ '_' options_map.amap_name];
end
if iload==0,
if isempty(dir(xdir))
mkdir(xdir)
end
if ~options_.nograph,
hfig=dyn_figure(options_,'name',['Reduced Form Mapping: ', namy,' vs. ', namx]); hist(y0,30), title([namy,' vs. ', namx],'interpreter','none')
dyn_saveas(hfig,[xdir,filesep, namy,'_vs_', namx],options_);
end
% gsa_ = gsa_sdp_dyn(y0, x0, -2, [],[],[],1,fname, pnames);
nrun=length(y0);
nest=min(250,nrun);
nfit=min(1000,nrun);
% dotheplots = (nfit<=nest);
gsa_ = gsa_sdp(y0(1:nest), x0(1:nest,:), 2, [],[-1 -1 -1 -1 -1 0],[],0,[fname,'_est'], pnames);
if nfit>nest,
gsa_ = gsa_sdp(y0(1:nfit), x0(1:nfit,:), -2, gsa_.nvr*nest^3/nfit^3,[-1 -1 -1 -1 -1 0],[],0,fname, pnames);
% gsa_ = gsa_sdp(y0(1:nest), x0(1:nest,:), 2, [],[-1 -1 -1 -1 -1 0],[],0,[fname,'_est'], pnames);
[ys,is] = sort(y0);
istep = ceil(nrun/nest);
iest = is(floor(istep/2):istep:end);
nest = length(iest);
irest = is(setdiff([1:nrun],[floor(istep/2):istep:nrun]));
istep = ceil(length(irest)/(nfit-nest));
ifit = union(iest, irest(1:istep:end));
if ~ismember(irest(end),ifit),
ifit = union(ifit, irest(end));
end
nfit=length(ifit);
% ifit = union(iest, irest(randperm(nrun-nest,nfit-nest)));
% ifit = iest;
% nfit=nest;
ipred = setdiff([1:nrun],ifit);
if ilog,
[y1, tmp, isig, lam] = log_trans_(y0(iest));
y1 = log(y0*isig+lam);
end
save([fname,'.mat'],'gsa_')
[sidum, iii]=sort(-gsa_.si);
gsa_.x0=x00(1:nfit,:);
if ~options_.nograph,
hfig=gsa_sdp_plot(gsa_,fname,pnames,iii(1:min(12,np)));
if options_.nodisplay
close(hfig);
hfig=dyn_figure(options_,'name',options_map.figtitle);
subplot(221)
if ilog,
hist(y1,30),
else
hist(y0,30),
end
title(options_map.title,'interpreter','none')
subplot(222)
if ilog,
hc = cumplot(y1);
else
hc = cumplot(y0);
end
set(hc,'color','k','linewidth',2)
title([options_map.title ' CDF'],'interpreter','none')
end
gsa_.x0=x0(1:nfit,:);
gsa0 = ss_anova(y0(iest), x0(iest,:), 1);
if ilog,
[gsa22, gsa1, gsax] = ss_anova_log(y1(iest), x0(iest,:), isig, lam, gsa0);
end
% if (gsa1.out.bic-gsa0.out.bic) < 10,
% y00=y0;
% gsa00=gsa0;
% gsa0=gsa1;
% y0=y1;
% ilog=1;
% end
if nfit>nest,
% gsa_ = gsa_sdp(y0(1:nfit), x0(1:nfit,:), -2, gsa_.nvr*nest^3/nfit^3,[-1 -1 -1 -1 -1 0],[],0,fname, pnames);
nvr = gsa0.nvr*nest^3/nfit^3;
nvr(gsa0.stat<2) = gsa0.nvr(gsa0.stat<2)*nest^5/nfit^5;
gsa_ = ss_anova(y0(ifit), x0(ifit,:), 1, 0, 2, nvr);
if ilog
gsa0 = gsa_;
nvr1 = gsa1.nvr*nest^3/nfit^3;
nvr1(gsa1.stat<2) = gsa1.nvr(gsa1.stat<2)*nest^5/nfit^5;
nvrx = gsax.nvr*nest^3/nfit^3;
nvrx(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
[gsa22, gsa1, gsax] = ss_anova_log(y1(ifit), x0(ifit,:), isig, lam, gsa0, [nvr1' nvrx']);
% gsa1 = ss_anova(y1(ifit), x0(ifit,:), 1, 0, 2, nvr);
% gsa2=gsa1;
% gsa2.y = gsa0.y;
% gsa2.fit = (exp(gsa1.fit)-lam)*isig;
% gsa2.f0 = mean(gsa2.fit);
% gsa2.out.SSE = sum((gsa2.fit-gsa2.y).^2);
% gsa2.out.bic = gsa2.out.bic-nest*log(gsa1.out.SSE)+nest*log(gsa2.out.SSE);
% gsa2.r2 = 1-cov(gsa2.fit-gsa2.y)/cov(gsa2.y);
% for j=1:np,
% gsa2.fs(:,j) = exp(gsa1.fs(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
% gsa2.f(:,j) = exp(gsa1.f(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
% gsa2.si(j) = var(gsa2.f(:,j))/var(gsa2.y);
% end
% nvr = gsax.nvr*nest^3/nfit^3;
% nvr(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
% gsax = ss_anova([gsa2.y-gsa2.fit], x0(ifit,:), 1, 0, 2, nvr);
% gsa22=gsa2;
% gsa22.fit = gsa2.fit+gsax.fit;
% gsa22.f0 = mean(gsa22.fit);
% gsa22.out.SSE = sum((gsa22.fit-gsa22.y).^2);
% gsa22.out.bic = nest*log(gsa22.out.SSE/nest) + (gsax.out.df+gsa2.out.df-1)*log(nest);
% gsa22.r2 = 1-sum((gsa22.fit-gsa22.y).^2)/sum((gsa22.y-mean(gsa22.y)).^2);
% for j=1:np,
% gsa22.fs(:,j) = gsa2.fs(:,j)+gsax.fs(:,j);
% gsa22.f(:,j) = gsa2.f(:,j)+gsax.f(:,j);
% gsa22.si(j) = var(gsa22.f(:,j))/var(gsa22.y);
% end
gsa_ = gsa22;
end
else
if ilog
gsa_ = gsa22;
else
gsa_ = gsa0;
end
end
save([fname,'_map.mat'],'gsa_')
[sidum, iii]=sort(-gsa_.si);
gsa_.x0=x00(ifit,:);
if ~options_.nograph,
hmap=gsa_sdp_plot(gsa_,[fname '_map'],pnames,iii(1:min(12,np)));
set(hmap,'name',options_map.amap_title);
end
gsa_.x0=x0(ifit,:);
% copyfile([fname,'_est.mat'],[fname,'.mat'])
if ~options_.nograph,
hfig=dyn_figure(options_,'name',['Reduced Form Mapping: ' namy,'_vs_', namx,'_fit']);
plot(y0(1:nfit),[gsa_.fit y0(1:nfit)],'.'),
title([namy,' vs. ', namx,' fit'],'interpreter','none')
dyn_saveas(hfig,[xdir,filesep, namy,'_vs_', namx,'_fit'],options_);
figure(hfig);
subplot(223),
plot(y0(ifit),[gsa_.fit y0(ifit)],'.'),
r2 = gsa_.r2;
% if ilog,
% plot(y00(ifit),[log_trans_(gsa_.fit,'',isig,lam) y00(ifit)],'.'),
% r2 = 1 - cov(log_trans_(gsa_.fit,'',isig,lam)-y00(ifit))/cov(y00(ifit));
% else
% plot(y0(ifit),[gsa_.fit y0(ifit)],'.'),
% r2 = gsa_.r2;
% end
title(['Learning sample fit - R2=' num2str(r2,2)],'interpreter','none')
if nfit<nrun,
npred=[nfit+1:nrun];
yf = ss_anova_fcast(x0(npred,:), gsa_);
hfig=dyn_figure(options_,'name',['Reduced Form Mapping: ' namy,'_vs_', namx,'_pred']);
plot(y0(npred),[yf y0(npred)],'.'),
title([namy,' vs. ', namx,' pred'],'interpreter','none')
dyn_saveas(hfig,[xdir,filesep, namy,'_vs_', namx,'_pred'],options_);
if ilog,
yf = ss_anova_fcast(x0(ipred,:), gsa1);
yf = log_trans_(yf,'',isig,lam)+ss_anova_fcast(x0(ipred,:), gsax);
else
yf = ss_anova_fcast(x0(ipred,:), gsa_);
end
yn = y0(ipred);
r2 = 1-cov(yf-yn)/cov(yn);
subplot(224),
plot(yn,[yf yn],'.'),
title(['Out-of-sample prediction - R2=' num2str(r2,2)],'interpreter','none')
end
dyn_saveas(hfig,fname,options_);
if options_.nodisplay
close(hmap);
end
end
else
% gsa_ = gsa_sdp_dyn(y0, x0, 0, [],[],[],0,fname, pnames);
gsa_ = gsa_sdp(y0, x0, 0, [],[],[],0,fname, pnames);
% gsa_ = gsa_sdp(y0, x0, 0, [],[],[],0,fname, pnames);
load([fname,'_map.mat'],'gsa_')
if ~options_.nograph,
yf = ss_anova_fcast(x0, gsa_);
hfig=dyn_figure(options_,['Reduced Form Mapping: ' namy,'_vs_', namx,'_pred']);
hfig=dyn_figure(options_,'name',options_map.title);
plot(y0,[yf y0],'.'),
title([namy,' vs. ', namx,' pred'],'interpreter','none')
dyn_saveas(hfig,[xdir,filesep, namy,'_vs_', namx,'_pred'],options_);
title([namy,' vs ', namx,' pred'],'interpreter','none')
dyn_saveas(hfig,[fname '_pred'],options_);
end
end
% si = gsa_.multivariate.si;
si = gsa_.si;
return
function gsa2 = log2level_map(gsa1, isig, lam)
nest=length(gsa1.y);
np = size(gsa1.x0,2);
gsa2=gsa1;
gsa2.y = log_trans_(gsa1.y,'',isig,lam);
gsa2.fit = (exp(gsa1.fit)-lam)*isig;
gsa2.f0 = mean(gsa2.fit);
gsa2.out.SSE = sum((gsa2.fit-gsa2.y).^2);
gsa2.out.bic = gsa2.out.bic-nest*log(gsa1.out.SSE)+nest*log(gsa2.out.SSE);
gsa2.r2 = 1-cov(gsa2.fit-gsa2.y)/cov(gsa2.y);
for j=1:np,
gsa2.fs(:,j) = exp(gsa1.fs(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
gsa2.fses(:,j) = exp(gsa1.fs(:,j)+gsa1.fses(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0-gsa2.fs(:,j);
gsa2.f(:,j) = exp(gsa1.f(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
gsa2.si(j) = var(gsa2.f(:,j))/var(gsa2.y);
end
return
function [gsa22, gsa1, gsax] = ss_anova_log(y,x,isig,lam,gsa0,nvrs)
[nest, np]=size(x);
if nargin==6,
gsa1 = ss_anova(y, x, 1, 0, 2, nvrs(:,1));
else
gsa1 = ss_anova(y, x, 1);
end
gsa2 = log2level_map(gsa1, isig, lam);
if nargin >=5 && ~isempty(gsa0),
for j=1:np,
nvr2(j) = var(diff(gsa2.fs(:,j),2));
nvr0(j) = var(diff(gsa0.fs(:,j),2));
end
inda = find((gsa0.stat<2)&(gsa1.stat>2));
inda = inda(log10(nvr0(inda)./nvr2(inda))/2<0);
gsa1.nvr(inda)=gsa1.nvr(inda).*10.^(log10(nvr0(inda)./nvr2(inda)));
gsa1 = ss_anova(y, x, 1, 0, 2, gsa1.nvr);
gsa2 = log2level_map(gsa1, isig, lam);
end
if nargin==6,
gsax = ss_anova(gsa2.y-gsa2.fit, x, 1, 0, 2, nvrs(:,2));
else
gsax = ss_anova(gsa2.y-gsa2.fit, x, 1);
end
gsa22=gsa2;
gsa22.fit = gsa2.fit+gsax.fit;
gsa22.f0 = mean(gsa22.fit);
gsa22.out.SSE = sum((gsa22.fit-gsa22.y).^2);
gsa22.out.bic = nest*log(gsa22.out.SSE/nest) + (gsax.out.df+gsa2.out.df-1)*log(nest);
gsa22.r2 = 1-sum((gsa22.fit-gsa22.y).^2)/sum((gsa22.y-mean(gsa22.y)).^2);
for j=1:np,
gsa22.fs(:,j) = gsa2.fs(:,j)+gsax.fs(:,j);
gsa22.fses(:,j) = gsax.fses(:,j);
gsa22.f(:,j) = gsa2.f(:,j)+gsax.f(:,j);
gsa22.si(j) = var(gsa22.f(:,j))/var(gsa22.y);
end
return
function indmcf = redform_mcf(y0, x0, options_mcf, options_)
hfig=dyn_figure(options_,'name',options_mcf.amcf_title);
[post_mean, post_median, post_var, hpd_interval, post_deciles, ...
density] = posterior_moments(y0,1,0.9);
post_deciles = [-inf; post_deciles; inf];
for jt=1:10,
indy{jt}=find( (y0>post_deciles(jt)) & (y0<=post_deciles(jt+1)));
leg{jt}=[int2str(jt) '-dec'];
end
[proba, dproba] = stab_map_1(x0, indy{1}, indy{end}, [],0);
indmcf=find(proba<options_mcf.pvalue_ks);
[tmp,jtmp] = sort(proba(indmcf),2,'ascend');
indmcf = indmcf(jtmp);
nbr_par = length(indmcf);
nrow=ceil(sqrt(nbr_par+1));
ncol=nrow;
if nrow*(nrow-1)>nbr_par,
ncol=nrow-1;
end
cmap = colormap(jet(10));
for jx=1:nbr_par,
subplot(nrow,ncol,jx)
hold off
for jt=1:10,
h=cumplot(x0(indy{jt},indmcf(jx)));
set(h,'color', cmap(jt,:), 'linewidth', 2)
hold all,
end
title(options_mcf.param_names(indmcf(jx),:),'interpreter','none')
end
hleg = legend(leg);
aa=get(hleg,'Position');
aa(1)=1-aa(3)-0.02;
aa(2)=0.02;
set(hleg,'Position',aa);
if ~isoctave
annotation('textbox', [0.25,0.01,0.5,0.05], ...
'String', options_mcf.title, ...
'Color','black',...
'FontWeight','bold',...
'interpreter','none',...
'horizontalalignment','center');
end
dyn_saveas(hfig,[options_mcf.OutputDirectoryName filesep options_mcf.fname_,'_',options_mcf.amcf_name],options_);
return

10
matlab/gsa/stab_map_.m
View File

@ -561,7 +561,7 @@ if length(iunstable)>0 || length(iwrong)>0,
options_mcf.beha_title = 'unique Stable Saddle-Path';
options_mcf.nobeha_title = 'NO unique Stable Saddle-Path';
options_mcf.title = 'unique solution';
mcf_analysis(lpmat, istable, itmp, options_mcf, options_)
mcf_analysis(lpmat, istable, itmp, options_mcf, options_);
if ~isempty(iindeterm),
itmp = itot(find(~ismember(itot,iindeterm)));
@ -570,7 +570,7 @@ if length(iunstable)>0 || length(iwrong)>0,
options_mcf.beha_title = 'NO indeterminacy';
options_mcf.nobeha_title = 'indeterminacy';
options_mcf.title = 'indeterminacy';
mcf_analysis(lpmat, itmp, iindeterm, options_mcf, options_)
mcf_analysis(lpmat, itmp, iindeterm, options_mcf, options_);
end
if ~isempty(ixun),
@ -580,7 +580,7 @@ if length(iunstable)>0 || length(iwrong)>0,
options_mcf.beha_title = 'NO explosive solution';
options_mcf.nobeha_title = 'explosive solution';
options_mcf.title = 'instability';
mcf_analysis(lpmat, itmp, ixun, options_mcf, options_)
mcf_analysis(lpmat, itmp, ixun, options_mcf, options_);
end
inorestriction = istable(find(~ismember(istable,irestriction))); % what went wrong beyong prior restrictions
@ -592,7 +592,7 @@ if length(iunstable)>0 || length(iwrong)>0,
options_mcf.beha_title = 'NO inability to find a solution';
options_mcf.nobeha_title = 'inability to find a solution';
options_mcf.title = 'inability to find a solution';
mcf_analysis(lpmat, itmp, iwrong, options_mcf, options_)
mcf_analysis(lpmat, itmp, iwrong, options_mcf, options_);
end
if ~isempty(irestriction),
@ -602,7 +602,7 @@ if length(iunstable)>0 || length(iwrong)>0,
options_mcf.beha_title = 'prior IRF/moment calibration';
options_mcf.nobeha_title = 'NO prior IRF/moment calibration';
options_mcf.title = 'prior restrictions';
mcf_analysis([lpmat0 lpmat], irestriction, inorestriction, options_mcf, options_)
mcf_analysis([lpmat0 lpmat], irestriction, inorestriction, options_mcf, options_);
iok = irestriction(1);
x0 = [lpmat0(iok,:)'; lpmat(iok,:)'];
else

23
matlab/plot_identification.m
View File

@ -247,13 +247,30 @@ else
hist(log10(idelre.cond))
title('log10 of Condition number in the LRE model')
dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_ident_COND' ],options_);
options_mcf.pvalue_ks = 0.1;
options_mcf.pvalue_corr = 0.001;
options_mcf.alpha2 = 0;
options_mcf.param_names = name;
options_mcf.fname_ = M_.fname;
options_mcf.OutputDirectoryName = IdentifDirectoryName;
options_mcf.beha_title = 'LOW condition nbr';
options_mcf.nobeha_title = 'HIGH condition nbr';
options_mcf.amcf_name = 'MC_HighestCondNumberLRE';
options_mcf.amcf_title = 'MC Highest Condition Number LRE Model';
options_mcf.title = 'MC Highest Condition Number LRE Model';
ncut=floor(SampleSize/10*9);
[dum,is]=sort(idelre.cond);
[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberLRE', 1, [], IdentifDirectoryName, 0.1,'MC Highest Condition Number LRE Model');
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, options_);
options_mcf.amcf_name = 'MC_HighestCondNumberModel';
options_mcf.amcf_title = 'MC Highest Condition Number Model Solution';
options_mcf.title = 'MC Highest Condition Number Model Solution';
[dum,is]=sort(idemodel.cond);
[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberModel', 1, [], IdentifDirectoryName, 0.1,'MC Highest Condition Number Model Solution');
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, options_);
options_mcf.amcf_name = 'MC_HighestCondNumberMoments';
options_mcf.amcf_title = 'MC Highest Condition Number Model Moments';
options_mcf.title = 'MC Highest Condition Number Model Moments';
[dum,is]=sort(idemoments.cond);
[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberMoments', 1, [], IdentifDirectoryName, 0.1,'MC Highest Condition Number Model Moments');
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, options_);
% [proba, dproba] = stab_map_1(idemoments.Mco', is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments_vs_Mco', 1, [], IdentifDirectoryName);
% for j=1:nparam,
% % ibeh=find(idemoments.Mco(j,:)<0.9);