Re: st: test for clustering in instrumental variables settings

[Stas Kolenikov <skolenik@gmail.com>]

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.

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
>
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>



-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
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