bma_g1 — Bayesian model averaging
rbma_g = bma_g1(y,x,ndraw,burnin,mcmc,g,nvmax)
y = dependent variable vector
x = explanatory variables
ndraw = # of draws to carry out
burnin = # of burn-in MCMC simulation
mcmc = name of the MCMC algorithm (jump_g or mc3_g)
g = value of g-prior (default = 1/max(nobs,k^2))
nvmax = max number of variable allowed in each model
rbma_g = a results tlist with
-rbma_g('meth') = 'bma g-prior'
-rbma_g('nmod') = # of models visited during sampling
-rbma_g('beta') = bhat averaged over all models
-rbma_g('mprob') = posterior prob of each model
-rbma_g('vprob') = posterior prob of each variable
-rbma_g('model') = indicator variables for each model (nmod x k)
-rbma_g('yhat') = yhat averaged over all models
-rbma_g('resid') = residuals based on yhat averaged over models
-rbma_g('sige') = averaged over all models
-rbma_g('nobs') = nobs
-rbma_g('nvar') = # of exogenous
-rbma_g('y') = y data vector
-rbma_g('x') = y data vector
-rbma_g('visit') = visits to each model during sampling (nmod x 1)
-rbma_g('time') = time taken for MCMC sampling
-rbma_g('ndraw') = # of MCMC sampling draws
-rbma_g('burnin')= # of burn-in MCMC simulation
-rbma_g('gprior')= value of g-prior