diff --git a/matlab/gsa/filt_mc_.m b/matlab/gsa/filt_mc_.m
index fbc25214fabc23869a57d0c7483d0cc4ea9a9ddd..4724801352dc653769b735bfe603c406b15adb04 100644
--- a/matlab/gsa/filt_mc_.m
+++ b/matlab/gsa/filt_mc_.m
@@ -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';
- 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);
+ atitle = 'RMSE MC: Log Likelihood Kernel';
+ end
+ 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));
diff --git a/matlab/gsa/log_trans_.m b/matlab/gsa/log_trans_.m
index d8c726c5ff3d33db26260aad00c3187ae92fbc89..2f48c7156788a8ee1af3b5bb912d4ce96d86f850 100644
--- a/matlab/gsa/log_trans_.m
+++ b/matlab/gsa/log_trans_.m
@@ -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
diff --git a/matlab/gsa/mcf_analysis.m b/matlab/gsa/mcf_analysis.m
index 82d7bfb7faa989670b92a66fed9e596b2992f3b1..cb7e92a10531e2d1493c34db7f5254652362210b 100644
--- a/matlab/gsa/mcf_analysis.m
+++ b/matlab/gsa/mcf_analysis.m
@@ -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,
diff --git a/matlab/gsa/redform_map.m b/matlab/gsa/redform_map.m
index 7421e1c923d6efdd079a4223876e4955ebd0dc57..448b1a634d0ab96706aa239efc3adc431c338d82 100644
--- a/matlab/gsa/redform_map.m
+++ b/matlab/gsa/redform_map.m
@@ -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
+
+ 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(1:nfit,:);
+ 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
diff --git a/matlab/gsa/stab_map_.m b/matlab/gsa/stab_map_.m
index b813aa367b91071a49e922b4997b2063a2171444..876a75e8a1b3e11000b1284366177755975e37fe 100644
--- a/matlab/gsa/stab_map_.m
+++ b/matlab/gsa/stab_map_.m
@@ -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
diff --git a/matlab/plot_identification.m b/matlab/plot_identification.m
index 4f9fcf98ac362410d2abc9157c68853aa7705276..549224ef0dfae6b783857acdd8e702353d916111 100644
--- a/matlab/plot_identification.m
+++ b/matlab/plot_identification.m
@@ -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);