Series x=j y=sas_probn / group=i curvelabel Series x=j y=mc_probn / group=i curvelabel Yvector = randnormal( 100000, mvector, smatrix) Īnd then run this PROC SGPLOT to compare the MC results with the PROBBNRM computations. Depending on the application, you might want to simulate 1 million obs, especially if you are interested in behavior in the tails.Ĭhange your simulation code to use 100,000 like this: The Monte Carlo error is proportional to 1/sqrt(n), so I'd use at least 100,000 simulated observations. There's nothing intrinsically wrong with your simulation code, except that the sample size is too small for this computation. Rc = run_macro('mc_mvn',x1,x2,rho,seed,probn) Rc = run_macro('mc_mvt',x1,x2,rho,df,seed,probt) *** FCMP FUNCTION TO RUN MACRO FROM WITHIN DATASTEP *** Yvector = randnormal(10000, mvector, smatrix) *** MACRO TO GENERATE MONTE CARLO INTEGRATED MVN CDF ESTIMATE IN IML *** Yvector = randmvt(10000, &df., mvector, smatrix) *** MACRO TO GENERATE MONTE CARLO INTEGRATED MVT CDF ESTIMATE IN IML *** *** TEST BASIC MONTE CARLO INEGRATION ***

I have tried this for the bivariate T (see embedded SAS program) but I don’t think this would be accurate and quick enough (I compared the accuracy of the process for the MVN in the code with the SAS function PROBBNRM and it isn’t great). Rather than try to digest what looks to be very complicated separation algebra in the paper, I hoped someone may have a solution already?Ī very simple way to do it would be a MC integration algorithm generating large numbers of MVT values from IML and averaging them. R has a package mvtnorm that can do it, and I think it is based methods in the paper by Genz & Bretz (attached). Has anyone got a reliable and accurate way to do this in SAS? I suspect IML would be needed hence posting here. I would like to be able to calculate the CDF of the Bivariate (and multivariate) T distribution.

Cdf files r