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Re: st: Kenward-Roger method in Stata?

From   Maarten buis <>
Subject   Re: st: Kenward-Roger method in Stata?
Date   Fri, 4 Jun 2010 03:33:35 -0700 (PDT)

--- On Fri, 4/6/10, Seed, Paul wrote:
> SAS's command PROC MIXED has an option " ddfm=KR " 
> which implements Kenward & Roger (1997), 
> improving on the asymptotic estimation of 
> SE, CI significance etc.  There is some evidence that
> it performs well with missing data and small samples 
> Has anyone attempted to implement this in Stata, for 
> either -xtgee- or -xtmixed-?  

Not that I know, but I would start with trying to figure
out whether you would actually need it. One way of 
figureing out whether this is true for your dataset is

1) estimate your model of interest, for the purpose of this
   simulation this will be the estimates in the "populaiton".
2) use -bsample- to draw a sample from your data
3) estimate your model of interest
4) test whether the parameter of interest is equal to the 
   "population"-parameter, and return the p-value.
5) repeat steps 2-4 many times (use the -simulate- command
   for that.)

If the standard standard errors work wel for your data, then
these p-value should follow a uniform distribution.  If I am
allowed some shameless self-promotion, I would say that I like
-hangroot-  for that. (see: -ssc d hangroot- and the 7th graph 
on <>). You can
also look at the coverage: if 5% of the samples have a p-value
less than .05. As the information necesary for computing this
coverage is contained in only 5% of the samples, you need a 
great many of those to get a reliable estimate, say 10,000, 
which means you would expect around 500 rejections.

Hope this helps,

Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen


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