🚀 go-pugleaf

In article <1993Jan4.112016.12341@mic.ucla.edu> iwelch@agsm.ucla.edu (Ivo Welch) writes:
>
>Well, let me correct my last statement(s).
>
>	Yes, SAS can compute the White (1980) heteroskedasticity-adjusted
>	covariance matrix. However, it can
>
>	neither	[a] output this matrix to a data set for further processing
>	nor	[b] use it to compute White se's or T-stats
>
>Essentially,  this procedure is  infeasible    for anything but   small   data
>problems.  If you  have 2,000  coefficients, be  prepared to  wade through   4
>million numbers, and compute the square root of 2,000 of them by hand.
>
>(Of course, I could  start learning IML programming,  and  compute  everything
>myself.  However, if I  have to  program, my  preference  is to do so in other
>languages [e.g. C, Fortran, Gauss,...] instead.)
>
>/ivo welch

My order of preference would be Gauss, C++, Fortran.  And you can order
Gauss with LINREG which computes Het.-Cons. standard errors for single
and multiple equation regression, and MAXLIK which will do the same for
user provided likelihood functions.


-- 
Ronald Schoenberg                    fax: 206-727-6521
University of Washington           email: rons@u.washington.edu