Re: st: ivregress with2sls and clustered standard errors

[Stas Kolenikov <skolenik@gmail.com>]

Yes, that's the aspect I mentioned first: you can either do clustered
standard errors/GMM weight matrix, or school fixed effect dummies.

On Sat, Mar 26, 2011 at 4:22 PM, Averett, Susan L
<averetts@lafayette.edu> wrote:
> I didn't have any luck with the wmatrix option. In that case, I actually am told that I have no observations. Could this be because I am using school fixed effects as well as trying to cluster on schools?
>
> Susan Averett
> ----- Original Message -----
> From: "Stas Kolenikov" <skolenik@gmail.com>
> To: statalist@hsphsun2.harvard.edu
> Sent: Saturday, March 26, 2011 4:35:21 PM
> Subject: Re: st: ivregress with2sls and clustered standard errors
>
> Susan Averett is working with education data estimating an
> instrumental variable model with standard errors clustered at the
> school level, and is looking for ways to conduct overidentification
> tests.
>
> On Sat, Mar 26, 2011 at 3:08 PM, Averett, Susan L
> <averetts@lafayette.edu> wrote:
>> I am trying to run a model using ivregress with 2sls as my estimator.
>> I have a data set of individuals who are clustered in various schools.
>> I am using school fixed effects in my model but I am trying to cluster
>> my standard errors on the school variable
>
> If you used fixed effect dummies for schools, then you would have more
> regressors than you have clusters, which should've triggered an error
> message somewhere.
>
>> AND also obtain the overidentification tests from
>>
>> estat overid
>>
>> However, when I use the cluster command, I get an error:
>>
>> robust tests of overidentifying restrictions after 2SLS
>>  estimation not available with cluster-robust standard errors
>>
>> I do not get this same error if I use robust standard errors.
>
> Apparently, the way -overid- is currently coded, it does not even try
> to compute the overidentification test with clustered standard errors.
> Either it was too difficult to code, or, more likely, there was (and
> in all likelihood, still no) solid theory regarding overid tests with
> clustered data.
>
>> What is the option if this error is received? Is is possible I do not
>> need to cluster my standard errors?
>
> I see three ways to get out of this.
>
> 1. At a descriptive level, you can compare the standard errors with
> and without clustering (-robust- only). If they are not drastically
> different (indicating that the schools are rather alike, and student
> homogeneity within schools does not affect your results much), then
> you can probably use the test without clustering, although of course
> you would need to describe everything in detail to convince the
> readers of your paper.
>
> 2. You can reverse engineer -overid-, figure how this test is
> obtained, and compute it by hand.
>
> 3. You will get the overidentification test from -gmm- (assuming you
> are using Stata 11 which you did not state). See examples in the
> manual as to how run the analogue of -ivregress-; you would need to
> specify the -wmatrix(cluster school) to obtain both estimates that are
> efficient under clustering, and the overid test (using -estat
> overid-).
>
> --
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
>
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-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.

*
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