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plotPredictiveDensitiesPaper2021.m
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%% plot predictive densities for figures and charts
clear
close all
fclose all;
%% load em toolboxes
warning('off','MATLAB:handle_graphics:exceptions:SceneNode')
path(pathdef)
addpath matlabtoolbox/emtools/
addpath matlabtoolbox/emtexbox/
addpath matlabtoolbox/emgibbsbox/
addpath matlabtoolbox/emeconometrics/
addpath matlabtoolbox/emstatespace/
%% setup
resultsdir = pwd;
startSam = [];
datalabel = 'fredMD16-2021-04';
modellabel = strcat(datalabel, '-censoredYields');
p = 12; % number of lags
% modellabel = strcat(modellabel, '-covidRobustPrior');
fontsize = 24;
wrap = [];
initwrap
doConnectDots = false;
datestrfmt = 'mmmm yyyy';
%#ok<*UNRCH>
%% select plots
doFullYlist = false;
doFig3 = true;
doFig4 = true;
%% load matfiles (oos runs)
do2020 = '';
prettylabelCONST = 'CONST';
mfilename = sprintf('%s-CONST%s-p%d.mat', modellabel, do2020, p);
matCONST = matfile(fullfile(resultsdir, mfilename), 'writable', false);
prettylabelSV = 'SV';
mfilename = sprintf('%s-SV%s-p%d.mat', modellabel, do2020, p);
matSV = matfile(fullfile(resultsdir, mfilename), 'writable', false);
% prettylabelSVtdof = 'SV-t(5)';
% mfilename = sprintf('%s-SVt-dof5%s-p%d.mat', modellabel, do2020, p);
% matSVtdof = matfile(fullfile(resultsdir, mfilename), 'writable', false);
prettylabelSVt = 'SV-t';
mfilename = sprintf('%s-SVt%s-p%d.mat', modellabel, do2020, p);
matSVt = matfile(fullfile(resultsdir, mfilename), 'writable', false);
prettylabelSVO = 'SVO';
mfilename = sprintf('%s-SVOmax20%s-p%d.mat', modellabel, do2020, p);
matSVO = matfile(fullfile(resultsdir, mfilename), 'writable', false);
prettylabelSVobar = 'SVo';
mfilename = sprintf('%s-SVobarmax20%s-p%d.mat', modellabel, do2020, p);
matSVobar = matfile(fullfile(resultsdir, mfilename), 'writable', false);
prettylabelSVoutMiss = 'SV-OutMiss';
Ofactor = 5;
mfilename = sprintf('%s-SVnanO%d%s-p%d.mat', modellabel, Ofactor, do2020, p);
matSVoutMiss = matfile(fullfile(resultsdir, mfilename), 'writable', false);
prettylabelSVdummy = 'SV-Dummies';
dummyPrecision = 0;
mfilename = sprintf('%s-SV-COVIDEACH-precision%d-p%d.mat', modellabel, dummyPrecision, p);
matSVdummy = matfile(fullfile(resultsdir, mfilename), 'writable', false);
% prettylabelSVOtdof = 'SVO-t(9)';
% mfilename = sprintf('%s-SVOtmax20-dof9%s-p%d.mat', modellabel, do2020, p);
% matSVOtdof = matfile(fullfile(resultsdir, mfilename), 'writable', false);
prettylabelSVOt = 'SVO-t';
mfilename = sprintf('%s-SVOtmax20%s-p%d.mat', modellabel, do2020, p);
matSVOt = matfile(fullfile(resultsdir, mfilename), 'writable', false);
%% grab a few things
N = matCONST.N;
ydates = matCONST.ydates;
fcstNhorizons = matCONST.fcstNhorizons;
Tjumpoffs = matCONST.Tjumpoffs;
np = 12;
ncode = matCONST.ncode;
tcode = matCONST.tcode;
setQuantiles = matCONST.setQuantiles;
Ylabels = fredMDprettylabel(ncode);
Ylabelslong = Ylabels;
Ylabelslong(ismember(tcode, [2 5])) = strcat(Ylabelslong(ismember(tcode, [2 5])), ' (APR growth)');
%% patch in data
data = matCONST.data;
if ~isequal(data, matSVO.data)
error houston
end
if ~isequal(data, matSVt.data)
error houston
end
if ~isequal(data, matSVoutMiss.data)
error houston
end
if ~isequal(data, matSV.data)
error houston
end
if ~isequal(data, matSVdummy.data)
error houston
end
if ~isequal(data, matSVOt.data)
error houston
end
Tdata = length(data);
%% construct fcstdates
yearStart = year(ydates(1));
yearStop = year(ydates(end)) + 9;
if matSV.doQuarterly
fcstdates = genrQdates(yearStart, yearStop);
else
fcstdates = genrMdates(yearStart, yearStop, 1);
end
ndx = find(fcstdates == ydates(1));
if isempty(ndx)
error houston
end
fcstdates = fcstdates(ndx:end);
ndxCI = find(ismember(matCONST.setQuantiles, normcdf([-1 1]) * 100));
%% collect densities
thismat = matCONST;
fcstTailsCONST = squeeze(thismat.fcstYquantiles(:,:,ndxCI,:));
fcstMidCONST = thismat.fcstYmedian;
thismat = matSV;
fcstTailsSV = NaN(size(fcstTailsCONST));
fcstMidSV = NaN(size(fcstMidCONST));
ndx = find(ismember(Tjumpoffs, thismat.Tjumpoffs));
fcstTailsSV(:,:,:,ndx) = squeeze(thismat.fcstYquantiles(:,:,ndxCI,:));
fcstMidSV(:,:,ndx) = thismat.fcstYmedian;
thismat = matSVO;
fcstTailsSVO = NaN(size(fcstTailsCONST));
fcstMidSVO = NaN(size(fcstMidCONST));
ndx = find(ismember(Tjumpoffs, thismat.Tjumpoffs));
fcstTailsSVO(:,:,:,ndx) = squeeze(thismat.fcstYquantiles(:,:,ndxCI,:));
fcstMidSVO(:,:,ndx) = thismat.fcstYmedian;
thismat = matSVobar;
fcstTailsSVobar = NaN(size(fcstTailsCONST));
fcstMidSVobar = NaN(size(fcstMidCONST));
ndx = find(ismember(Tjumpoffs, thismat.Tjumpoffs));
fcstTailsSVobar(:,:,:,ndx) = squeeze(thismat.fcstYquantiles(:,:,ndxCI,:));
fcstMidSVobar(:,:,ndx) = thismat.fcstYmedian;
thismat = matSVt;
fcstTailsSVt = NaN(size(fcstTailsCONST));
fcstMidSVt = NaN(size(fcstMidCONST));
ndx = find(ismember(Tjumpoffs, thismat.Tjumpoffs));
fcstTailsSVt(:,:,:,ndx) = squeeze(thismat.fcstYquantiles(:,:,ndxCI,:));
fcstMidSVt(:,:,ndx) = thismat.fcstYmedian;
% thismat = matSVtdof;
% fcstTailsSVtdof = NaN(size(fcstTailsCONST));
% fcstMidSVtdof = NaN(size(fcstMidCONST));
% ndx = find(ismember(Tjumpoffs, thismat.Tjumpoffs));
% fcstTailsSVtdof(:,:,:,ndx) = squeeze(thismat.fcstYquantiles(:,:,ndxCI,:));
% fcstMidSVtdof(:,:,ndx) = thismat.fcstYmedian;
thismat = matSVOt;
fcstTailsSVOt = NaN(size(fcstTailsCONST));
fcstMidSVOt = NaN(size(fcstMidCONST));
ndx = find(ismember(Tjumpoffs, thismat.Tjumpoffs));
fcstTailsSVOt(:,:,:,ndx) = squeeze(thismat.fcstYquantiles(:,:,ndxCI,:));
fcstMidSVOt(:,:,ndx) = thismat.fcstYmedian;
% thismat = matSVOtdof;
% fcstTailsSVOtdof = NaN(size(fcstTailsCONST));
% fcstMidSVOtdof = NaN(size(fcstMidCONST));
% ndx = find(ismember(Tjumpoffs, thismat.Tjumpoffs));
% fcstTailsSVOtdof(:,:,:,ndx) = squeeze(thismat.fcstYquantiles(:,:,ndxCI,:));
% fcstMidSVOtdof(:,:,ndx) = thismat.fcstYmedian;
thismat = matSVoutMiss;
fcstTailsSVoutMiss = NaN(size(fcstTailsCONST));
fcstMidSVoutMiss = NaN(size(fcstMidCONST));
ndx = find(ismember(Tjumpoffs, thismat.Tjumpoffs));
fcstTailsSVoutMiss(:,:,:,ndx) = squeeze(thismat.fcstYquantiles(:,:,ndxCI,:));
fcstMidSVoutMiss(:,:,ndx) = thismat.fcstYmedian;
outliermissYmid = NaN(length(ydates), N, length(Tjumpoffs));
outliermissYtails = NaN(length(ydates), N, length(ndxCI), length(Tjumpoffs));
outliermissYmid(:,:,ndx) = matSVoutMiss.drawsYmid;
outliermissYtails(:,:,:,ndx) = matSVoutMiss.drawsYtails(:,:,ndxCI,:);
thismat = matSVdummy;
fcstTailsSVdummy = NaN(size(fcstTailsCONST));
fcstMidSVdummy = NaN(size(fcstMidCONST));
ndx = find(ismember(Tjumpoffs, thismat.Tjumpoffs));
fcstTailsSVdummy(:,:,:,ndx) = squeeze(thismat.fcstYquantiles(:,:,ndxCI,:));
fcstMidSVdummy(:,:,ndx) = thismat.fcstYmedian;
%% list ylabels and ncodes
if ~isempty(wrap)
tabname = sprintf('datalist-%s.tex', datalabel);
filename = fullfile(wrap.dir, tabname);
fid = fopen(filename, 'wt');
fprintf(fid, '\\begin{center}\n');
fprintf(fid, '\\begin{tabular}{lll}\n');
fprintf(fid, '\\toprule\n');
fprintf(fid, 'Variable & FRED-MD code & transformation ');
fprintf(fid, '\\\\\n');
fprintf(fid, '\\midrule\n');
for n = 1 : N
fprintf(fid, '%s ', Ylabels{n});
fprintf(fid, '& %s ', ncode{n});
switch tcode(n)
case 1
% % no transformation
case 2
fprintf(fid, '& %s\n', '\ensuremath{\Delta x_t}');
case 4
fprintf(fid, '& %s\n', '\ensuremath{\log(x_t)}');
case 5
fprintf(fid, '& %s\n', '\ensuremath{\Delta\log(x_t) \cdot 1200}');
otherwise
error houston
end
fprintf(fid, '\\\\\n');
end
fprintf(fid, '\\bottomrule\n');
fprintf(fid, '\\end{tabular}\n');
fprintf(fid, '\\end{center}\n');
fprintf(fid, '\n');
% fprintf(fid, 'Note: ');
% fprintf(fid, 'Data obtained from the %s vintage of FRED-MD. ', strtok(csv_in, '.'));
% fprintf(fid, 'Monthly observations ');
% fprintf(fid, 'from %s to %s. ', datestryymm(ydates(1)), datestryymm(ydates(end)));
% fprintf(fid, '%s \n', fredMDtcodeNote);
fclose(fid);
type(filename)
latexwrapper(wrap, 'add', 'sidetab', tabname, [])
end
darkgreen = [0 .75 0];
black = [0 0 0];
tickdates = [datenum(2019,[6 12],1),datenum(2020,[6 12],1), datenum(2021,[6 12],1), datenum(2022,[6 12],1), datenum(2023,[6 12],1)];
%% FIG3: plot CONST vs SV vs SVOt
if doFig3
if doFullYlist
ylist = 1:N;
else
ylist = [1 6];
end
ndxRange = find(any(ydates(Tjumpoffs) == datenum(2020,[3 4 6],1),2));
ndxRange = ndxRange(:)'; % ensure it is a row vector
close all
thisPlotLabel = 'predictiveDensityCONSTvsSVvsSVOt';
for n = ylist
for ndxT = ndxRange
jumpoff = Tjumpoffs(ndxT);
thisdateT = ydates(jumpoff);
%% compare predictive densities (jumpoff)
jj = find(fcstdates == ydates(jumpoff));
% allthesedates = fcstdates(jj:jj+fcstNhorizons);
theseFcstdates = fcstdates(jj+(1:fcstNhorizons));
% theseJumpoffdates = fcstdates(jj+(-np:0));
% if ~isequal(theseJumpoffdates, ydates(jj+(-np:0)))
% error houston
% end
theseRealized = NaN(fcstNhorizons, 1);
ndx = jj+(1:fcstNhorizons);
ndx = ndx(ndx <= Tdata);
theseRealized(1 : length(ndx)) = data(ndx,n);
% theseFcstdatesCumJumpoff = [theseJumpoffdates(end); theseFcstdates];
hanni = NaN(3,1);
%% plot
thisfig = figure;
set(thisfig,'defaultLegendAutoUpdate','off');
ax = gca;
set(ax, 'fontsize', fontsize)
hold on
hanni(1) = plotCI(fcstMidCONST(n,:,ndxT), squeeze(fcstTailsCONST(n,:,:,ndxT)), ...
theseFcstdates, [], 'k-', 'linewidth', 3);
hanni(2) = plot(theseFcstdates,fcstMidSV(n,:,ndxT), 'r--', 'linewidth', 3);
plot(theseFcstdates,squeeze(fcstTailsSV(n,:,:,ndxT)), 'r--', 'linewidth', 2);
hanni(3) = plot(theseFcstdates,fcstMidSVOt(n,:,ndxT), 'b-.', 'linewidth', 3);
plot(theseFcstdates,squeeze(fcstTailsSVOt(n,:,:,ndxT)), 'b-.', 'linewidth', 2);
% plot(theseJumpoffdates, data(jj+(-np:0),n), '-', 'color', darkgreen, 'linewidth', 2);
% if doConnectDots
% plot([theseJumpoffdates(end) theseFcstdates(1)], [data(jj,n) fcstMidCONST(n,1,ndxT)], ':', 'color', black, 'linewidth', 2);
% end
% switch n
% case 1
% ylim([-400 400])
% case 6
% ylim([-200 100])
% end
if min(ylim) < 0 && max(ylim) > 0, plotOrigin, end
xlim(theseFcstdates([1 end]))
xticks(tickdates)
datetick('x', 'yyyy:mm', 'keeplimits', 'keepticks')
ax.XAxis.MinorTick = 'on';
wrapthisfigureBW(thisfig, sprintf('%s-%s-%s', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
ht = title(sprintf('%s', datestr(thisdateT, datestrfmt)));
wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHTITLE', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
hl = legend(hanni, prettylabelCONST, prettylabelSV, prettylabelSVOt, ...
'location', 'best');
wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHTITLELEGEND', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
delete(ht)
wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHLEGEND', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
delete(hl);
ht = title(sprintf('%s', datestr(thisdateT, datestrfmt)));
hd = plot(theseFcstdates, theseRealized, '*', 'color', darkgreen, 'linewidth', 2, 'markersize', 10);
% plot(theseFcstdates, theseRealized, ':', 'color', darkgreen, 'linewidth', 2);
wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHTITLEDATA', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], false)
delete(ht);
wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHDATA', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
hl = legend([hanni; hd], prettylabelCONST, prettylabelSV, prettylabelSVOt, 'realized', ...
'location', 'best');
wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHLEGENDDATA', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
% tabulatePanel(wrap, thisPlotLabel, n, ndxT, thisdateT, theseFcstdates, ...
% prettylabelCONST, prettylabelSV, prettylabelSVOt, ...
% fcstMidCONST, fcstMidSV, fcstMidSVOt, ...
% fcstTailsCONST, fcstTailsSV, fcstTailsSVOt, ...
% ncode, ndxCI, setQuantiles)
end
end
end
%% FIG4 plot SVOt vs SVoutmiss
if doFig4
if doFullYlist
ylist = 1:N;
else
ylist = [1 5 6];
end
ndxRange = find(any(ydates(Tjumpoffs) == [datenum(2020,[9 12],1), datenum(2021, 3,1)],2));
ndxRange = ndxRange(:)'; % ensure it is a row vector
close all
thisPlotLabel = 'predictiveDensitySVOtOutmiss';
for n = ylist
for ndxT = ndxRange
jumpoff = Tjumpoffs(ndxT);
thisdateT = ydates(jumpoff);
%% identify outlier data per sample end
dataO = data(1:jumpoff,:);
dev = abs(dataO - median(dataO));
iqr = range(prctile(dataO, [25 75]));
dataOnan = dev > Ofactor * iqr;
outlier = dataO;
outlier(~dataOnan) = NaN;
dataO(dataOnan) = NaN;
%% compare predictive densities (jumpoff)
jj = find(fcstdates == ydates(jumpoff));
allthesedates = fcstdates(jj-np:jj+fcstNhorizons);
theseFcstdates = fcstdates(jj+(1:fcstNhorizons));
theseJumpoffdates = fcstdates(jj+(-np:0));
if ~isequal(theseJumpoffdates, ydates(jj+(-np:0)))
error houston
end
theseRealized = NaN(fcstNhorizons+1, 1);
ndx = jj+(0:fcstNhorizons);
ndx = ndx(ndx <= Tdata);
theseRealized(1 : length(ndx)) = data(ndx,n);
theseFcstdatesCumJumpoff = [theseJumpoffdates(end); theseFcstdates];
%% plot
hanni = NaN(2,1);
thisfig = figure;
set(thisfig,'defaultLegendAutoUpdate','off');
ax = gca;
set(ax, 'fontsize', fontsize)
hold on
hanni(1) = plotCI(fcstMidSVoutMiss(n,:,ndxT), squeeze(fcstTailsSVoutMiss(n,:,:,ndxT)), ...
theseFcstdates, [], 'k--', 'linewidth', 3);
hanni(2) = plot(theseFcstdates,fcstMidSVOt(n,:,ndxT), 'b-', 'linewidth', 1);
plot(theseFcstdates,squeeze(fcstTailsSVOt(n,:,:,ndxT)), 'b-', 'linewidth', 1/2);
% plotCI(outliermissYmid(jj+(-np:0),n, ndxT), squeeze(outliermissYtails(jj+(-np:0),n, :, ndxT)), ...
% theseJumpoffdates, [], 'k:', 'linewidth', 3);
xndx = jj+(-np:0);
outmissNdx = dataOnan(xndx,n);
if any(outmissNdx)
outmissStart = find(outmissNdx,1,'first');
outmissStop = find(outmissNdx,1,'last');
outmissStart = max(1, outmissStart-1);
outmissStop = min(np+1, outmissStop+1);
xndx = xndx(outmissStart:outmissStop);
plotCI(outliermissYmid(xndx,n, ndxT), squeeze(outliermissYtails(xndx,n, :, ndxT)), ...
theseJumpoffdates(outmissStart:outmissStop), [], 'k--', 'linewidth', 3);
end
hlagged = plot(theseJumpoffdates, data(jj+(-np:0),n), ':', 'color', darkgreen, 'linewidth', 3);
hmiss = plot(theseJumpoffdates, outlier(jj+(-np:0),n), 'o', 'color', [0 0 0], ...
'markersize', 8, 'linewidth', 2);
if doConnectDots
plot([theseJumpoffdates(end) theseFcstdates(1)], [data(jj,n) fcstMidSVoutMiss(n,1,ndxT)], ':', 'color', black, 'linewidth', 2);
end
if min(ylim) < 0 && max(ylim) > 0, plotOrigin, end
xlim(allthesedates([1 end]))
xticks(tickdates)
datetick('x', 'yyyy', 'keeplimits')
ax.XAxis.MinorTick = 'on';
% wrapthisfigureBW(thisfig, sprintf('%s-%s-%s', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
%
% % ht = title(sprintf('%s', datestr(thisdateT, datestrfmt)));
% % wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHTITLE', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
%
% hl = legend(hanni, prettylabelSVoutMiss, prettylabelSVOt, ...
% 'location', 'best');
% % wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHTITLELEGEND', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
% %
% % delete(ht)
% wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHLEGEND', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
%
% delete(hl);
% ht = title(sprintf('%s', datestr(thisdateT, datestrfmt)));
hd = plot(theseFcstdates, theseRealized(2:end), '*', 'color', darkgreen, 'linewidth', 2, 'markersize', 10);
% wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHTITLEDATA', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
% delete(ht);
wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHDATA', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
hl = legend([hanni; hlagged; hmiss; hd], prettylabelSVoutMiss, prettylabelSVOt, ...
'past data', 'omitted in OutMiss', 'forecast realizations', ...
'location', 'best');
wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHLEGENDDATA', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], true)
% ht = title(sprintf('%s', datestr(thisdateT, datestrfmt)));
% wrapthisfigureBW(thisfig, sprintf('%s-%s-%s-WITHTITLELEGENDDATA', thisPlotLabel, ncode{n}, datestr(thisdateT, 28)), wrap, [], [], [], [], false)
end
end
end
%% finish
dockAllFigures
finishwrap
finishscript