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RE: st: test for clustering in instrumental variables settings


From   "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: test for clustering in instrumental variables settings
Date   Thu, 25 Feb 2010 16:55:33 -0000

Stas, Sergio,

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu 
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of 
> Stas Kolenikov
> Sent: Thursday, February 25, 2010 3:52 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: test for clustering in instrumental 
> variables settings
> 
> With a binary endogenous variables, you need to think first 
> whether the
> effect in the main dependent variable is due to the 0/1 value of the
> endogenous variable, or due to the propensity (the linear 
> predictor part)
> associated with that variable.
> 
> I don't think there are formal tests for whether you do or do 
> not need the
> clustered standard errors. But the folk wisdom is, if you 
> have clusters then
> you have to use the clustered standard errors (which will 
> likely dilute the
> significance of your results compared to the assumption of the i.i.d.
> data). In a somewhat related problem of testing for 
> heteroskedasticity in
> linear regression, econometricians use White's information matrix test
> (-estat imtest- after -regress-). In all likelihood, it can 
> be generalized
> to the clustered data situation, but I am not aware of 
> whether that was done
> or not.

Gabor Kezdi has done it.  Here's one version of his paper:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=596988

I had a go at implementing it (with Austin Nichols) in Stata.  We have a
working beta version (somewhere...).

--Mark

> 
> On Wed, Feb 24, 2010 at 4:39 PM, Sergio I Prada 
> <sprada1@umbc.edu> wrote:
> 
> > Dear users:
> >
> > I am trying to come up with a good way to test whether I 
> need to use SEs
> > clustered in my estimation. But I could not.
> > I have a binary outcome and a binary treatment variable. My 
> treatment
> > variable is endogenous and I have two good instruments. The 
> model includes
> > covariates.
> > The problem is that my treatment variable is whether 
> treatment at certain
> > type of hospital, and my clusters are hospitals. So with no 
> variation at
> > the cluster level on the endogenous variable I cannot use 
> tricks like
> > adding averages or deviations of the endogenous variable 
> (as recommended
> > in the multilevel literature).
> > I am using instead recursive biprobit models, and of course 
> the problem is
> > that the significance of my results change with and without 
> SEs clustered
> > at hospital level.
> > I have 69 clusters, and they vary a lot by size (from 2 
> patients in one
> > hospital to 122 in other)
> > Are any of you aware of a way to test whether I have to 
> adjust SEs at the
> > cluster
> > level.
> >
> > --
> > Sergio
> >
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/help.cgi?search
> > *   http://www.stata.com/support/statalist/faq
> > *   http://www.ats.ucla.edu/stat/stata/
> >
> 
> 
> 
> -- 
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
> 
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
> 


-- 
Heriot-Watt University is a Scottish charity
registered under charity number SC000278.


*
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