goVAR.m

%% Standard linear VAR of <<Forecasting with Shadow-Rate VARs>>
% by Carriero, Clark, Marcellino and Mertens (2021)
% The working paper and supplementary appendices are available here: https://doi.org/10.26509/frbc-wp-202109
%
% Recursive estimation of quasi-real-time forecasts

%#ok<*NOSEL>
%#ok<*DISPLAYPROG>
%#ok<*UNRCH>
%#ok<*DATNM>
%#ok<*DATST>

%% 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/
addpath matlabtoolbox/empbsbox/

%% Initial operations
clear; close all; clc;

tic % start clocking time

% NOTE: to utilize multiple cores, "parpool" must be started prior to executing the script
%       (or MATLAB must be enabled to launch parpool as needed)
%        otherwise, only a single core gets used (and Nstreams will return 1 after executing the next line)
Nstreams    = max(1,getparpoolsize);
rndStreams  = parallel.pool.Constant(RandStream('Threefry'));

%% set parameters for VAR and MCMC

datalabel           = 'fredsxMD20exYield-2022-09';
doQuarterly         = false;
doRATSprior         = true;
MCMCdraws           = 1e3;                % Final number of MCMC draws after burn in
fcstNdraws          = 10 * MCMCdraws;     % draws sampled from predictive density

ELBbound    = 0.25;

if doQuarterly
    p  = 4;
    np = 4; % number of periods per year, used for calibrating priors
    datalabel = strcat(datalabel, '-quarterly');
else
    p  = 12;
    np = 12;
end

% SED-PARAMETERS-HERE


doStoreXL           = false; %#ok<*NASGU>
check_stationarity  = 0;                  % Truncate nonstationary draws? (1=yes)
Compute_diagnostics = false;              % compute Inefficiency Factors and Potential


doLoMem             = true; % do not store memory intensive stuff, just do oos forecasts

doPlotData          = false;

samStart            = [];                 % truncate start of sample if desired (leave empty if otherwise)



if doLoMem
    doStoreXL = false;
end

%% load data

% load CSV file
dum=importdata(sprintf('%s.csv', datalabel),',');


ydates=dum.data(3:end,1);
% Variable names
ncode=dum.textdata(1,2:end);
% Transformation codes (data are already transformed)
tcode  =dum.data(1,2:end);

cumcode=logical(dum.data(2,2:end));
cumcode(tcode == 5) = 1;

% Data
data=dum.data(3:end,2:end);

setShadowYields

Nyields   = length(ndxYIELDS);

Nshadowrates = length(ndxSHADOWRATE);
Tdata = length(ydates);

Ylabels = fredMDprettylabel(ncode);

%% process settings
N = size(data,2);
Kbvar = N * p + 1; % number of regressors per equation
K = Kbvar;

modellabel = 'standardVARAR1SV';

if ELBbound ~= 0.25
    modellabel = strcat(modellabel, sprintf('-ELB%d', ELBbound * 1000));
end

if doRATSprior
    modellabel = strcat(modellabel, '-RATSbvarshrinkage');
end




% truncate start of sample (if desired)
if ~isempty(samStart)
    ndx  = ydates >= samStart;
    data   = data(ndx,:);
    ydates = ydates(ndx);
    Tdata  = length(ydates);
end

% define oos jump offs
Tjumpoffs   = find(ydates > datenum(2008,12,1));

Njumpoffs = length(Tjumpoffs);

% other settings

setQuantiles = [.5, 2.5, 5, normcdf(-1) * 100, 25 , 75,  (1 - normcdf(-1)) * 100, 95, 97.5, 99.5];
Nquantiles   = length(setQuantiles);
fractiles = [normcdf(-1) * 100, 100 - normcdf(-1) * 100];
ndxCI68   = ismember(setQuantiles, fractiles);
ndxCI90   = ismember(setQuantiles, [5 95]);
ndxCI     = ndxCI68 | ndxCI90;

%% mean for Minnesota prior
setMinnesotaMean


%% allocate memory for tracking random states
randomStates      = NaN(17, Njumpoffs);
%% allocate memory for out-of-sample forecasts

fcstNhorizons     = 48;  % number of steps forecasted (1:fcstNhorizon)

% fcstYdraws        = NaN(N,fcstNhorizons,fcstNdraws,Njumpoffs);
fcstYrealized     = NaN(N,fcstNhorizons,Njumpoffs);
fcstYhatRB        = NaN(N,fcstNhorizons,Njumpoffs); % predictive mean (linear RB)

% linear forecasts
fcstYhat          = NaN(N,fcstNhorizons,Njumpoffs); % predictive mean
fcstYmedian       = NaN(N,fcstNhorizons,Njumpoffs); % predictive median
fcstYhaterror     = NaN(N,fcstNhorizons,Njumpoffs);
fcstYmederror     = NaN(N,fcstNhorizons,Njumpoffs);
fcstYcrps         = NaN(N,fcstNhorizons,Njumpoffs);
fcstYquantiles    = NaN(N,fcstNhorizons,Nquantiles, Njumpoffs);

fcstYmvlogscoreDraws = NaN(fcstNdraws,Njumpoffs); % one-step ahead only
fcstYmvlogscore      = NaN(1,Njumpoffs); % one-step ahead only
fcstYmvlogscoreELBdraws = NaN(fcstNdraws,Njumpoffs); % one-step ahead only
fcstYmvlogscoreELB      = NaN(1,Njumpoffs); % one-step ahead only

fcstYmvlogscoreXdraws = NaN(fcstNdraws,Njumpoffs); % one-step ahead only
fcstYmvlogscoreX      = NaN(1,Njumpoffs); % one-step ahead only
fcstYmvlogscoreIdraws = NaN(fcstNdraws,Njumpoffs); % one-step ahead only
fcstYmvlogscoreI      = NaN(1,Njumpoffs); % one-step ahead only


% cumulated forecasts
fcstYcumrealized     = NaN(N,fcstNhorizons,Njumpoffs);
fcstYcumhat          = NaN(N,fcstNhorizons,Njumpoffs); % predictive mean
fcstYcummedian       = NaN(N,fcstNhorizons,Njumpoffs); % predictive median
fcstYcumhaterror     = NaN(N,fcstNhorizons,Njumpoffs);
fcstYcummederror     = NaN(N,fcstNhorizons,Njumpoffs);
fcstYcumcrps         = NaN(N,fcstNhorizons,Njumpoffs);
fcstYcumquantiles    = NaN(N,fcstNhorizons,Nquantiles, Njumpoffs);



% censored forecasts
fcstYcensorhat          = NaN(N,fcstNhorizons,Njumpoffs); % predictive mean
fcstYcensormedian       = NaN(N,fcstNhorizons,Njumpoffs); % predictive median
fcstYcensorhaterror     = NaN(N,fcstNhorizons,Njumpoffs);
fcstYcensormederror     = NaN(N,fcstNhorizons,Njumpoffs);
fcstYcensorcrps         = NaN(N,fcstNhorizons,Njumpoffs);
fcstYcensorquantiles    = NaN(N,fcstNhorizons,Nquantiles, Njumpoffs);

% shadow forecasts
fcstYshadowhat          = NaN(N,fcstNhorizons,Njumpoffs); % predictive mean
fcstYshadowmedian       = NaN(N,fcstNhorizons,Njumpoffs); % predictive median
fcstYshadowhaterror     = NaN(N,fcstNhorizons,Njumpoffs);
fcstYshadowmederror     = NaN(N,fcstNhorizons,Njumpoffs);
fcstYshadowcrps         = NaN(N,fcstNhorizons,Njumpoffs);
fcstYshadowquantiles    = NaN(N,fcstNhorizons,Nquantiles, Njumpoffs);



[PAImedian, PAImean, PAIstdev]    = deal(NaN(K, N, Njumpoffs));
PAIquantiles                      = NaN(K, N, Nquantiles, Njumpoffs);
[hRHOmedian, hRHOmean, hRHOstdev] = deal(NaN(N, Njumpoffs));
hRHOquantiles                     = NaN(N, Nquantiles, Njumpoffs);
[hBARmedian, hBARmean, hBARstdev] = deal(NaN(N, Njumpoffs));
hBARquantiles                     = NaN(N, Nquantiles, Njumpoffs);

%% allocate memory for MCMC output (ex forecast)
if ~doLoMem
    drawsPAI       = NaN(MCMCdraws, K, N, Njumpoffs);
    drawsPHI       = NaN(MCMCdraws, N*(N-1)/2+N, Njumpoffs);
    drawsINVA      = NaN(MCMCdraws, N, N, Njumpoffs);
    drawsSQRTHT    = NaN(MCMCdraws, Tdata, N, Njumpoffs);
    
    %% allocate memory for IRF and sum of FFR coeffs
    VMAmid  = NaN(N,N,fcstNhorizons,Njumpoffs);
    VMAtail = NaN(N,N,fcstNhorizons,Nquantiles,Njumpoffs);
    
    if ~isempty(ndxSHADOWRATE)
        sumFFRmid  = NaN(N,Njumpoffs);
        sumFFRtail = NaN(N,Nquantiles,Njumpoffs);
    end
    
end

drawsMaxVARroot = NaN(MCMCdraws, Njumpoffs);

%% start latexwrapper to collect results
titlename=sprintf('%s-%s-p%d', datalabel, modellabel, p);
if ~isempty(samStart)
    titlename = strcat(titlename, '-', datestr(samStart, 'yyyymmm'));
end
initwrap
% wrap = [];

%% plot input data
if doPlotData
    for n = 1 : N
        this = figure;
        plot(ydates, data(:,n))
        xtickdates(ydates)
        wrapthisfigure(this, sprintf('data%s', ncode{n}), wrap)
    end
end

%% loop over QRT estimates

% progressbar(0)
parfor ndxT = 1 : Njumpoffs % parfor
    
    
    TID   = parid;
    thisT = Tjumpoffs(ndxT);
    T     = thisT - p;
    
    thisStream           = rndStreams.Value;
    thisStream.Substream = ndxT;
    
    
    fprintf('loop %d, thisT %d, with TID %d\n', ndxT, thisT, TID)
    
    %% collect realized values (without cumulation)
    thisdata = data; % to avoid parfor warning
    yrealized = NaN(N, fcstNhorizons);
    for h = 1 : fcstNhorizons
        if thisT + h <= Tdata
            yrealized(:,h) = thisdata(thisT+h,:)';
        end
    end
    
    % set Funds Rate equal to ELB when at ELB
    % (Note: ELB may be set higher than actual funds rate readings, e.g. 25bp)
    if ~isempty(ELBbound)
        yieldsrealized             = yrealized(ndxSHADOWRATE,:);
        ndx                        = yieldsrealized < ELBbound;
        yieldsrealized(ndx)        = ELBbound;
        yrealized(ndxSHADOWRATE,:) = yieldsrealized;
    end

    [PAI_all, hRHO_all, hBAR_all, PHI_all, invA_all, sqrtht_all, ...
                ydraws, yhat, ...
                ycensordraws, ycensorhat, ...
                yshadowdraws, yshadowhat, ...
                yhatRB, logscoredraws, logscoreELBdraws, ...
                logscoreXdraws, logscoreIdraws ...
                ] = deal([]); % to avoid parfor warning
    %% MCMC sampler

    mcmcOK = false;
    while ~mcmcOK
        try % catch crashes and continue
            % launch mcmc sampler
            [PAI_all, hRHO_all, hBAR_all, PHI_all, invA_all, sqrtht_all, ...
                ydraws, yhat, ...
                ycensordraws, ycensorhat, ...
                yshadowdraws, yshadowhat, ...
                yhatRB, logscoredraws, logscoreELBdraws, ...
                logscoreXdraws, logscoreIdraws ...
                ] = mcmcVARAR1SV(thisT, MCMCdraws, p, np, data, ydates, ...
                minnesotaPriorMean, doRATSprior, ...
                ndxYIELDS, ELBbound, ...
                check_stationarity, ...
                yrealized, ...
                fcstNdraws, fcstNhorizons, thisStream, false); %#ok<PFBNS>
            mcmcOK = true;
        catch ME
            fprintf('Crash at TID %d, thisT %d\n', TID, thisT)
            fprintf('Error message: %s\n', ME.message)
            continue
        end
    end

    randomStates(:,ndxT) = thisStream.State;
	
    %% Convergence diagnostics
    if Compute_diagnostics
        % display('computing convergence diagnostics..')
        Diagnostics(sqrtht_all,invA_all,PAI_all,PHI_all,N,K,MCMCdraws);
    end
    
    %% compute out-of-sample forecasts
    
    % a word on parfor strategy:
    % to make matlab better see the intended use of sliced variabes, use
    % local temp variables and then copy those into the slices at end of
    % loop
    
    
    % cumulated forecasts
    ycumrealized            = yrealized;
    ycumdraws               = ydraws;
    ycumhat                 = yhat;
    ycumrealized(cumcode,:) = cumsum(ycumrealized(cumcode,:),2); % ./ (1:fcstNhorizons);
    ycumdraws(cumcode,:,:)  = cumsum(ycumdraws(cumcode,:,:),2); % ./ (1:fcstNhorizons);
    ycumhat(cumcode,:)      = cumsum(ycumhat(cumcode,:),2); % ./ (1:fcstNhorizons);
    
    % CRPS
    yCRPS = NaN(N,fcstNhorizons);
    for h = 1 : fcstNhorizons
        for n = 1 : N % loop over elements of Y
            yCRPS(n,h) = crpsDraws(yrealized(n,h), ydraws(n,h,:));
        end
    end
    
    ycumCRPS = NaN(N,fcstNhorizons);
    for h = 1 : fcstNhorizons
        for n = 1 : N % loop over elements of Y
            ycumCRPS(n,h) = crpsDraws(ycumrealized(n,h), ycumdraws(n,h,:));
        end
    end
    
    ycensorCRPS = NaN(N,fcstNhorizons);
    for h = 1 : fcstNhorizons
        for n = 1 : N % loop over elements of Y
            ycensorCRPS(n,h) = crpsDraws(yrealized(n,h), ycensordraws(n,h,:));
        end
    end
    
    yshadowCRPS = NaN(N,fcstNhorizons);
    for h = 1 : fcstNhorizons
        for n = 1 : N % loop over elements of Y
            yshadowCRPS(n,h) = crpsDraws(yrealized(n,h), yshadowdraws(n,h,:));
        end
    end
    
    
    
    %% collect PAI moments
    PAImedian(:,:,ndxT)       = squeeze(median(PAI_all,1));
    PAImean(:,:,ndxT)         = squeeze(mean(PAI_all,1));
    PAIstdev(:,:,ndxT)        = squeeze(std(PAI_all,1,1));
    PAIquantiles(:,:,:,ndxT)  = permute(prctile(PAI_all,setQuantiles,1), [2 3 1]);
    
    %% collect RHO moments
    hRHOmedian(:,ndxT)       = median(hRHO_all,1);
    hRHOmean(:,ndxT)         = mean(hRHO_all,1);
    hRHOstdev(:,ndxT)        = std(hRHO_all,1);
    hRHOquantiles(:,:,ndxT)  = transpose(prctile(hRHO_all,setQuantiles,1));
    
    %% collect BAR moments
    hBARmedian(:,ndxT)       = median(hBAR_all,1);
    hBARmean(:,ndxT)         = mean(hBAR_all,1);
    hBARstdev(:,ndxT)        = std(hBAR_all,1);
    hBARquantiles(:,:,ndxT)  = transpose(prctile(hBAR_all,setQuantiles,1));
    
    %% compute VMA / IRF
    theseMaxlambdas = NaN(MCMCdraws, 1); % placed before doLoMem to avoid parfor warning
    if doLoMem
        % setup companion form matrix
        comp                        = zeros(N * p);
        comp(N + 1 : end,1:N*(p-1)) = eye(N*(p-1));
        for m = 1 : MCMCdraws
            thisPAI = squeeze(PAI_all(m,:,:));
            comp(1:N,:) = thisPAI(2:Kbvar,:)';
            % compute maxLambda
            theseMaxlambdas(m) = max(abs(eig(comp)));
        end
    else
        drawsVMA = NaN(N, N, fcstNhorizons, MCMCdraws);
        
        % setup companion form matrix
        comp                        = zeros(N * p);
        comp(N + 1 : end,1:N*(p-1)) = eye(N*(p-1));
        for m = 1 : MCMCdraws
            thisPAI = squeeze(PAI_all(m,:,:));
            comp(1:N,:) = thisPAI(2:Kbvar,:)';
            % compute maxLambda
            theseMaxlambdas(m) = max(abs(eig(comp)));
            comppow = eye(N*p,N);
            for h = 1 : fcstNhorizons
                comppow = comp * comppow;
                drawsVMA(:,:,h,m) = comppow(1:N,1:N);
            end
        end
        
        VMAmid(:,:,:,ndxT)    = median(drawsVMA,4);
        VMAtail(:,:,:,:,ndxT) = prctile(drawsVMA, setQuantiles, 4);
        
        %% collect sum of FEDFUNDS coefficients
        if ~isempty(ndxSHADOWRATE)
            ndxFFRcoef = NaN(p,1);
            for i = 1 : p
                ndxFFRcoef(i) = (i - 1) * N + ndxSHADOWRATE;
            end
            sumFFR = NaN(N,MCMCdraws);
            
            for m = 1 : MCMCdraws
                thisPAI     = squeeze(PAI_all(m,2:Kbvar,:));
                sumFFR(:,m) = sum(thisPAI(ndxFFRcoef,:),1);
            end
            sumFFRmid(:,ndxT)    = median(sumFFR,2);
            sumFFRtail(:,:,ndxT) = prctile(sumFFR,setQuantiles,2);
        end
    end
    
    %% copy results into sliced variables
    
    fcstYrealized(:,:,ndxT) = yrealized;
    fcstYhatRB(:,:,ndxT)    = yhatRB;
    
    % predictive likelihood scores
    fcstYmvlogscoreDraws(:,ndxT)    = logscoredraws;
    maxlogscoredraw                 = max(logscoredraws);
    fcstYmvlogscore(:,ndxT)         = log(mean(exp(logscoredraws - maxlogscoredraw))) + maxlogscoredraw;
    
    fcstYmvlogscoreELBdraws(:,ndxT) = logscoreELBdraws;
    maxlogscoredraw                 = max(logscoreELBdraws);
    fcstYmvlogscoreELB(:,ndxT)      = log(mean(exp(logscoreELBdraws - maxlogscoredraw))) + maxlogscoredraw;
    
    fcstYmvlogscoreXdraws(:,ndxT)   = logscoreXdraws;
    maxlogscoredraw                 = max(logscoreXdraws);
    fcstYmvlogscoreX(:,ndxT)        = log(mean(exp(logscoreXdraws - maxlogscoredraw))) + maxlogscoredraw;
    
    fcstYmvlogscoreIdraws(:,ndxT)   = logscoreIdraws;
    maxlogscoredraw                 = max(logscoreIdraws);
    fcstYmvlogscoreI(:,ndxT)        = log(mean(exp(logscoreIdraws - maxlogscoredraw))) + maxlogscoredraw;
    
    % forecast
    ymed = median(ydraws,3);
    fcstYhat(:,:,ndxT)          = yhat;
    fcstYmedian(:,:,ndxT)       = ymed;
    fcstYhaterror(:,:,ndxT)     = yrealized - yhat;
    fcstYmederror(:,:,ndxT)     = yrealized - ymed;
    fcstYcrps(:,:,ndxT)         = yCRPS;
    fcstYquantiles(:,:,:,ndxT)  = prctile(ydraws, setQuantiles, 3);
    
    % cumulated forecast
    fcstYcumrealized(:,:,ndxT)    = ycumrealized;
    ymed = median(ycumdraws,3);
    fcstYcumhat(:,:,ndxT)          = ycumhat;
    fcstYcummedian(:,:,ndxT)       = ymed;
    fcstYcumhaterror(:,:,ndxT)     = ycumrealized - ycumhat;
    fcstYcummederror(:,:,ndxT)     = ycumrealized - ymed;
    fcstYcumcrps(:,:,ndxT)         = ycumCRPS;
    fcstYcumquantiles(:,:,:,ndxT)  = prctile(ycumdraws, setQuantiles, 3);
    
    % censored forecast
    ymed = median(ycensordraws,3);
    fcstYcensorhat(:,:,ndxT)          = ycensorhat;
    fcstYcensormedian(:,:,ndxT)       = ymed;
    fcstYcensorhaterror(:,:,ndxT)     = yrealized - ycensorhat;
    fcstYcensormederror(:,:,ndxT)     = yrealized - ymed;
    fcstYcensorcrps(:,:,ndxT)         = ycensorCRPS;
    fcstYcensorquantiles(:,:,:,ndxT)  = prctile(ycensordraws, setQuantiles, 3);
    
    % shadow forecast
    ymed = median(yshadowdraws,3);
    fcstYshadowhat(:,:,ndxT)          = yshadowhat;
    fcstYshadowmedian(:,:,ndxT)       = ymed;
    fcstYshadowhaterror(:,:,ndxT)     = yrealized - yshadowhat;
    fcstYshadowmederror(:,:,ndxT)     = yrealized - ymed;
    fcstYshadowcrps(:,:,ndxT)         = yshadowCRPS;
    fcstYshadowquantiles(:,:,:,ndxT)  = prctile(yshadowdraws, setQuantiles, 3);
    
    
    % copy mcmc output
    drawsMaxVARroot(:,ndxT)     = theseMaxlambdas;
    
    if ~doLoMem
        drawsPAI(:,:,:,ndxT)  = PAI_all;
        drawsPHI(:,:,ndxT)    = PHI_all;
        drawsINVA(:,:,:,ndxT) = invA_all;
        % prepare dummy to make parfor work
        dummy                      = NaN(MCMCdraws, Tdata, N);
        dummy(:, p+1:thisT, :)     = sqrtht_all;
        drawsSQRTHT(:, :, :, ndxT) = dummy;
    end
end

%% plot evolution of predictive densities
theseHorizons = [3 12 18 24];
for n = 1 : N
    
    thisfig = figure;
    
    for ii = 1 : length(theseHorizons)
        h = theseHorizons(ii);
        
        yrealized = squeeze(fcstYrealized(n,h,:));
        
        fcstMid   = squeeze(fcstYhat(n,h,:));
        fcstTails = squeeze(fcstYquantiles(n,h,ndxCI,:))';
        
        fcstCensorMid   = squeeze(fcstYcensorhat(n,h,:));
        fcstCensorTails = squeeze(fcstYcensorquantiles(n,h,ndxCI,:))';
        
        subplot(2,2,ii)
        
        hold on
        plotCI(fcstMid, fcstTails, ydates(Tjumpoffs), [], 'w-', 'linewidth', 1);
        plotCIlines(fcstCensorMid, fcstCensorTails, ydates(Tjumpoffs), [], 'r');
        
        
        plot(ydates(Tjumpoffs),yrealized, 'b-', 'linewidth', 2)
        
        title(sprintf('h=%d', h))
        sgtitle(sprintf('%s', Ylabels{n}))
        xtickdates(ydates(Tjumpoffs))
        
    end
    wrapthisfigure(thisfig, sprintf('predictiveDensity-%s', ncode{n}), wrap)
end

theseHorizons = [3 12 18 24];
for n = 1 : N
    
    thisfig = figure;
    
    for ii = 1 : length(theseHorizons)
        h = theseHorizons(ii);
        
        yrealized = squeeze(fcstYcumrealized(n,h,:));
        
        fcstMid   = squeeze(fcstYcumhat(n,h,:));
        fcstTails = squeeze(fcstYcumquantiles(n,h,ndxCI,:))';
        
        subplot(2,2,ii)
        
        hold on
        plotCI(fcstMid, fcstTails, ydates(Tjumpoffs), [], 'w-', 'linewidth', 1);
        
        plot(ydates(Tjumpoffs),yrealized, 'b-', 'linewidth', 2)
        
        title(sprintf('h=%d', h))
        sgtitle(sprintf('%s', Ylabels{n}))
        xtickdates(ydates(Tjumpoffs))
        
    end
    wrapthisfigure(thisfig, sprintf('predictiveDensityCum-%s', ncode{n}), wrap)
end



clear fcstTails



%% plot companion maxLambda
midMaxlambda = mean(drawsMaxVARroot,1);
medMaxlambda = median(drawsMaxVARroot,1);
tailsMaxlambda = prctile(drawsMaxVARroot, [5 95], 1);

this = figure;
hold on
plot(ydates(Tjumpoffs), midMaxlambda, 'k-', 'linewidth', 2)
plot(ydates(Tjumpoffs), medMaxlambda, 'r--', 'linewidth', 2)
plot(ydates(Tjumpoffs), tailsMaxlambda', 'k-', 'linewidth', 1)
xtickdates(ydates(Tjumpoffs))
wrapthisfigure(this, 'maxlambda', wrap)

%% collect computer system info

thisArch = computer('arch');
COMPUTERmatlab  = ver;
if ismac
    [~, COMPUTERsys]   = system('sysctl -a | grep machdep.cpu ', '-echo');
    [~, COMPUTERbrand] = system('sysctl -a | grep machdep.cpu | grep brand_string ', '-echo');
    if contains(COMPUTERbrand, 'M1') || contains(COMPUTERbrand, 'M2') || contains(COMPUTERbrand, 'M3') || contains(COMPUTERbrand, 'M4')
        COMPUTERbrand = 'AppleSilicon';
    else
        COMPUTERbrand = 'MacOSIntel';
    end
elseif isunix
    [~, COMPUTERsys] = system('cat /proc/cpuinfo ', '-echo');
    COMPUTERbrand = 'IntelUbuntu';
else % ispc
    COMPUTERsys   = 'Intel(R) Xeon(R) Gold 6248R CPU @ 3.0 GHz';
    COMPUTERbrand = 'WindowsXeon';
end

COMPUTERnstreams  = Nstreams;
COMPUTERtotaltime = toc; % stop clocking time

%% store qrt summary
matfilename = sprintf('%s-%s-p%d', datalabel, modellabel, p);
if ~isempty(samStart)
    matfilename = strcat(matfilename, '-', datestr(samStart, 'yyyymmm'));
end
varlist = {'data', 'ydates', 'p', 'Tjumpoffs', 'N', ...
    'ncode', 'tcode', 'cumcode', ...
    'fcst*', 'fcstNhorizons', ...
    '*Maxlambda*', ...
    'PAI*', 'hRHO*', 'hBAR*', ...
    'ndxSHADOWRATE', 'ndxYIELDS', 'ndxOTHERYIELDS', 'ELBbound',...
    'datalabel', 'modellabel', ...
    'doQuarterly', ...
    'setQuantiles', ...
    'MCMCdraws', ...
    'randomStates', ...
    'COMPUTER*'};
if ~doLoMem
    if ~isempty(ndxSHADOWRATE)
        varlist = cat(2, varlist, 'sumFFR*');
    end
    varlist = cat(2, varlist, 'VMA*');
end

if doStoreXL
    matfilename = sprintf('%s-%s-p%d-draws', datalabel, modellabel, p);
    save(matfilename, varlist{:}, 'draws*', '-v7.3');
end

clear *_all
save(matfilename, varlist{:}, '-v7.3');

%% wrap up
dockAllFigures
finishwrap