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Re: st: mmregress question


From   Steve Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: mmregress question
Date   Thu, 1 Dec 2011 18:11:48 -0500


The FAQ ask that you give precise references; in this case you are presumably referring to: Verardi, V., & Croux, C. (2009). Robust regression in Stata. Stata Journal, 9(3), 439-453. 

The article states that -mmregress- starts with an initial S-estimator and  (page 444) that

"The algorithm implemented in Stata for computing the S-estimator starts by randomly picking N subsets of p observation (defined as p-subset) where p is the number of regression parameters to estimate."

In other words, unless the same seed is -set- before each instance of -mmregress-,  one would expect the results to differ from run to run. 

Also, if there are indicator ("dummy") variables in your model, -mmregress- will have problems (p 445). If you have dummies,  you should instead run -msregress-, part of the same package, and list them only with the dummy() option.

If you still are having difficulties, I suggest that you contact the package's authors.

Steve






 


On Dec 1, 2011, at 12:59 PM, bcoric@efst.hr wrote:

I would like to ask for help with implementation of stata command mmregress.
I am doing simple cross section analysis. Since I have just 48
observations I am very much concerned about the possible influence of
outliers.
Previously I was using stata command rreg for such chases. However, I came
across the paper, Robust regression in Stata (2009), which argues that
rreg command does not have expected robustness properties and recommend
mmregress instead.
However, I faced some problems in its implementation. Namely, its
subsequent implementation to the same model leads to different values of
regression coefficients. Moreover, detected outliers also change.
I presume, that algorithm use iterative procedure taking previous
estimates as starting values. However, results are very different in
respect to the sign, size and significance of coefficients, and do not
converge after subsequent applications.
Does anyone can tell me is there anything, some procedure that I should
follow, to get robust (consistent) results.
Bruno Coric


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