% PURPOSE: An example of using bvarf_g(), % Gibbs estimates and forecast using % a vector autoregressive model % (with Minnesota prior) %--------------------------------------------------- % USAGE: bvarf_gd %--------------------------------------------------- load test.dat; % a test data set containing % monthly mining employment for % il,in,ky,mi,oh,pa,tn,wv % data covers 1982,1 to 1996,5 dates = cal(1982,1,12); % vnames = strvcat('il','in','ky','mi','oh','pa','tn','wv'); y = test(:,1:2); % use only two variables vnames = [' il', ' in']; [nobs neqs] = size(y); nlag = 2; % number of lags in var-model tight = 0.1; decay = 0.1; weight = 0.5; % symmetric weights % this is an example of using 1st-order contiguity % of the states as weights as in LeSage and Pan (1995) % `Using Spatial Contiguity as Bayesian Prior Information % in Regional Forecasting Models'' International Regional % Science Review, Volume 18, no. 1, pp. 33-53, 1995. w = [1.0 1.0 1.0 0.1 0.1 0.1 0.1 0.1 1.0 1.0 1.0 1.0 1.0 0.1 0.1 0.1 1.0 1.0 1.0 0.1 1.0 0.1 1.0 1.0 0.1 1.0 0.1 1.0 1.0 0.1 0.1 0.1 0.1 1.0 1.0 1.0 1.0 1.0 0.1 1.0 0.1 0.1 0.1 0.1 1.0 1.0 0.1 1.0 0.1 0.1 1.0 0.1 0.1 0.1 1.0 0.1 0.1 0.1 1.0 0.1 1.0 1.0 0.1 1.0]; % set up prior structure prior.tight = tight; prior.decay = decay; prior.weight = weight; prior.rval = 50; % homoscedastic prior % prior.rval = 4; % heteroscedastic prior ndraw = 1100; nomit = 100; begf = ical(1995,1,dates); % beginning forecast date nfor = 12; % # of forecasts endf = ical(1995,12,dates); % end forecast dates % straight bvar model in levels yfor1 = bvarf(y,nlag,nfor,begf,tight,weight,decay,[],dates); % estimate the model yfor2 = bvarf_g(y,nlag,nfor,begf,prior,ndraw,nomit,[],dates); rnames = 'Dates'; for i=begf:endf rnames = strvcat(rnames,tsdate(dates,i)); end; in.rnames = rnames; in.fmt = '%9.3f'; in.cnames = vnames; fprintf(1,'Bvar forecasts \n'); mprint(yfor1,in); fprintf(1,'Bvar Gibbs forecasts \n'); mprint(yfor2,in);