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st: RE: Why not always specify robust standard errors?

From   "Maarten Buis" <>
To   <>
Subject   st: RE: Why not always specify robust standard errors?
Date   Tue, 13 Feb 2007 17:43:17 +0100

--- Richard Williams wrote: 
> Why not always specify -robust- when using
> OLS regression?  My initial reaction is to say that you shouldn't
> relax restrictions unnecessarily; and there are various
> post-estimation commands where Stata will at least whine at you if
> you've used robust standard errors (e.g. -lrtest-).  But in practice,
> your model is probably at least a little mis-specified and/or there
> may be some degree of heteroskedasticity, so maybe robust is a good
> idea.  Any thoughts on the matter?

Part of the answer is discussed in [U] 20.14. I interpret it as 
follows: If your model is correct in every respect, the parameter 
estimates represent causal effects. Combine this with what we know 
about (simple random) sampling, and you get the normal standard 
errors. The robust standard errors start from a different point: 
the regression coefficient measures the difference in expected 
value for individuals that are one unit of the explanatory 
variable apart. It doesn't care whether this difference is 
(partially) causal or not. I tend to be rather sceptical about 
arguments that take estimates of causal effects too seriously; in 
the end we only observe differences in means, odds ratios, etc. As 
a consequence I cannot get very excited about this distinction, 
but others can.

Maarten L. Buis
Department of Social Research Methodology 
Vrije Universiteit Amsterdam 
Boelelaan 1081 
1081 HV Amsterdam 
The Netherlands

visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434 

+31 20 5986715

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