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- From: rons@hardy.u.washington.edu (Ronald Schoenberg)
- Newsgroups: sci.math.stat
- Subject: Re: SAS: White Heteroskedasticity-Consistent Standard Errors Wanted
- Message-ID: <1iaihiINN7og@shelley.u.washington.edu>
- Date: 4 Jan 93 23:51:14 GMT
- Article-I.D.: shelley.1iaihiINN7og
- References: <1992Dec28.222415.19246@mic.ucla.edu> <1992Dec29.102446.25292@mic.ucla.edu> <1993Jan4.112016.12341@mic.ucla.edu>
- Organization: University of Washington, Seattle
- Lines: 29
- NNTP-Posting-Host: hardy.u.washington.edu
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- 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
-