155 lines
6.6 KiB
Matlab
155 lines
6.6 KiB
Matlab
%function []= msstart_setup(options_)
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% ** ONLY UNDER UNIX SYSTEM
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%path(path,'/usr2/f1taz14/mymatlab')
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%===========================================
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% Exordium I
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%===========================================
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format short g % format
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%
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%options_.ms.freq = 4; % quarters or months
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%options_.ms.initial_year=1959; % beginning of the year
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%options_.ms.initial_subperiod=1; % begining of the quarter or month
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%options_.ms.final_year=2005; % final year
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%options_.ms.final_subperiod=4; % final month or quarter
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nData=(options_.ms.final_year-options_.ms.initial_year)*options_.ms.freq + (options_.ms.final_subperiod-options_.ms.initial_subperiod+1);
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% total number of the available data -- this is all you have
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%*** Load data and series
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%load datainf_argen.prn % the default name for the variable is "options_.ms.data".
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%load datacbogdpffr.prn
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%options_.ms.data = datacbogdpffr;
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%clear datacbogdpffr;
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[nt,ndv]=size(options_.data);
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if nt~=nData
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disp(' ')
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warning(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
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%disp(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
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disp('Press ctrl-c to abort')
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return
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end
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%--------
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%1 CBO output gap -- log(x_t)-log(x_t potential)
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%2 GDP deflator -- (P_t/P_{t-1})^4-1.0
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%2 FFR/100.
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options_.ms.vlist = [1:size(options_.varobs,1)]; % 1: U; 4: PCE inflation.
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options_.ms.varlist=cellstr(options_.varobs);
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%options_.ms.log_var = [ ]; % subset of "options_.ms.vlist. Variables in log level so that differences are in **monthly** growth, unlike R and U which are in annual percent (divided by 100 already).
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options_.ms.percent_var = [1:size(options_.varobs,1)]; % subset of "options_.ms.vlist"
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%options_.ms.restriction_fname='ftd_upperchol3v'; %Only used by msstart2.m.
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ylab = options_.ms.varlist;
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xlab = options_.ms.varlist;
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%----------------
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nvar = length(options_.ms.vlist); % number of endogenous variables
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nlogeno = length(options_.ms.log_var) % number of endogenous variables in options_.ms.log_var
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npereno = length(options_.ms.percent_var) % number of endogenous variables in options_.ms.percent_var
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if (nvar~=(nlogeno+npereno))
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disp(' ')
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warning('Check xlab, nlogeno or npereno to make sure of endogenous variables in options_.ms.vlist')
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disp('Press ctrl-c to abort')
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return
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elseif (nvar==length(options_.ms.vlist))
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nexo=1; % only constants as an exogenous variable. The default setting.
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elseif (nvar<length(options_.ms.vlist))
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nexo=length(options_.ms.vlist)-nvar+1;
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else
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disp(' ')
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warning('Make sure there are only nvar endogenous variables in options_.ms.vlist')
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disp('Press ctrl-c to abort')
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return
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end
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%------- A specific sample is considered for estimation -------
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yrStart=options_.ms.initial_year;
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qmStart=options_.ms.initial_subperiod;
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yrEnd=options_.ms.final_year;
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qmEnd=options_.ms.final_subperiod;
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%options_.ms.forecast = 4; % number of years for forecasting
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if options_.ms.forecast<1
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error('To be safe, the number of forecast years should be at least 1')
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end
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ystr=num2str(yrEnd);
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forelabel = [ ystr(3:4) ':' num2str(qmEnd) ' Forecast'];
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nSample=(yrEnd-yrStart)*options_.ms.freq + (qmEnd-qmStart+1);
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if qmEnd==options_.ms.freq
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E1yrqm = [yrEnd+1 1]; % first year and quarter (month) after the sample
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else
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E1yrqm = [yrEnd qmEnd+1]; % first year and quarter (month) after the sample
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end
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E2yrqm = [yrEnd+options_.ms.forecast qmEnd]; % end at the last month (quarter) of a calendar year after the sample
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[fdates,nfqm]=fn_calyrqm(options_.ms.freq,E1yrqm,E2yrqm); % forecast dates and number of forecast dates
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[sdates,nsqm] = fn_calyrqm(options_.ms.freq,[yrStart qmStart],[yrEnd qmEnd]);
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% sdates: dates for the whole sample (including options_.ms.nlags)
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if nSample~=nsqm
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warning('Make sure that nSample is consistent with the size of sdates')
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disp('Hit any key to continue, or ctrl-c to abort')
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pause
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end
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imstp = 4*options_.ms.freq; % <<>> impulse responses (4 years)
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nayr = 4; %options_.ms.forecast; % number of years before forecasting for plotting.
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%------- Prior, etc. -------
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%options_.ms.nlags = 4; % number of options_.ms.nlags
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%options_.ms.cross_restrictions = 0; % 1: cross-A0-and-A+ restrictions; 0: options_.ms.restriction_fname is all we have
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% Example for indxOres==1: restrictions of the form P(t) = P(t-1).
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%options_.ms.contemp_reduced_form = 0; % 1: contemporaneous recursive reduced form; 0: restricted (non-recursive) form
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%options_.ms.real_pseudo_forecast = 0; % 1: options_.ms.real_pseudo_forecast forecasts; 0: real time forecasts
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%options_.ms.bayesian_prior = 1; % 1: Bayesian prior; 0: no prior
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indxDummy = options_.ms.bayesian_prior; % 1: add dummy observations to the data; 0: no dummy added.
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%options_.ms.dummy_obs = 0; % No dummy observations for xtx, phi, fss, xdatae, etc. Dummy observations are used as an explicit prior in fn_rnrprior_covres_dobs.m.
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%if indxDummy
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% options_.ms.dummy_obs=nvar+1; % number of dummy observations
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%else
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% options_.ms.dummy_obs=0; % no dummy observations
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%end
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%=== The following mu is effective only if options_.ms.bayesian_prior==1.
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mu = zeros(6,1); % hyperparameters
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mu = zeros(6,1); % hyperparameters
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mu(1) = 0.57;
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mu(2) = 0.13;
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mu(3) = 0.1;
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mu(4) = 1.5; %1.4 or 1.5, faster decay, produces much better inflation forecast.
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mu(5) = 5; %10;
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mu(6) = 5; %10;
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% mu(1): overall tightness and also for A0;
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% mu(2): relative tightness for A+;
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% mu(3): relative tightness for the constant term;
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% mu(4): tightness on lag decay; (1)
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% mu(5): weight on nvar sums of coeffs dummy observations (unit roots);
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% mu(6): weight on single dummy initial observation including constant
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% (cointegration, unit roots, and stationarity);
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%
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%
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hpmsmd = [0.0; 0.0];
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indxmsmdeqn = [0; 0; 0; 0]; %This option disenable using this in fn_rnrprior_covres_dobs.m
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tdf = 3; % degrees of freedom for t-dist for initial draw of the MC loop
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nbuffer = 100; % a block or buffer of draws (buffer) that is saved to the disk (not memory)
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ndraws1=1*nbuffer; % 1st part of Monte Carlo draws
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ndraws2=10*ndraws1 % 2nd part of Monte Carlo draws
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seednumber = 0; %7910; %472534; % if 0, random state at each clock time
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% good one 420 for [29 45], [29 54]
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if seednumber
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randn('state',seednumber);
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rand('state',seednumber);
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else
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randn('state',fix(100*sum(clock)));
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rand('state',fix(100*sum(clock)));
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end
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% nstarts=1 % number of starting points
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% imndraws = nstarts*ndraws2; % total draws for impulse responses or forecasts
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%<<<<<<<<<<<<<<<<<<<
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