1#MCMC 2BayesmConstant.keep = 1 #keep every keepth draw for MCMC routines 3BayesmConstant.nprint = 100 #print the remaining time on every nprint'th draw 4BayesmConstant.RRScaling = 2.38 #Roberts and Rosenthal optimal scaling constant 5BayesmConstant.w = .1 #fractional likelihood weighting parameter 6 7#Priors 8BayesmConstant.A = .01 #scaling factor for the prior precision matrix 9BayesmConstant.nuInc = 3 #Increment for nu 10BayesmConstant.a = 5 #Dirichlet parameter for mixture models 11BayesmConstant.nu.e = 3.0 #degrees of freedom parameter for regression error variance prior 12BayesmConstant.nu = 3.0 #degrees of freedom parameter for Inverted Wishart prior 13BayesmConstant.agammaprior = .5 #Gamma prior parameter 14BayesmConstant.bgammaprior = .1 #Gamma prior parameter 15 16#DP 17BayesmConstant.DPalimdef=c(.01,10) #defines support of 'a' distribution 18BayesmConstant.DPnulimdef=c(.01,3) #defines support of nu distribution 19BayesmConstant.DPvlimdef=c(.1,4) #defines support of v distribution 20BayesmConstant.DPIstarmin = 1 #expected number of components at lower bound of support of alpha 21BayesmConstant.DPpower = .8 #power parameter for alpha prior 22BayesmConstant.DPalpha = 1.0 #intitalized value for alpha draws 23BayesmConstant.DPmaxuniq = 200 #storage constraint on the number of unique components 24BayesmConstant.DPSCALE = TRUE #should data be scaled by mean,std deviation before posterior draws 25BayesmConstant.DPgridsize = 20 #number of discrete points for hyperparameter priors 26 27#Mathematical Constants 28BayesmConstant.gamma = .5772156649015328606 29 30#BayesBLP 31BayesmConstant.BLPVOmega = matrix(c(1,0.5,0.5,1),2,2) #IW prior parameter of correlated shocks in IV bayesBLP 32BayesmConstant.BLPtol = 1e-6