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Re: st: cluster option in qreg


From   Austin Nichols <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: cluster option in qreg
Date   Wed, 8 Apr 2009 13:01:01 -0400

See also the threads at
http://www.stata.com/statalist/archive/2007-09/msg00147.html
http://www.stata.com/statalist/archive/2008-03/msg01222.html

You should, of course, run some simulations to assess the
finite-sample performance of whatever estimator you wind up using,
with various assumed effect sizes and whatever pathology best captures
the need to "correct the standard errors for potential correlation
between observations" in your case.  Note that in OLS, correlated
errors within group do not necessitate a cluster-robust SE unless X is
also correlated within group, and the cluster-robust SE only helps you
if X is still uncorrelated with the errors...

On Wed, Apr 8, 2009 at 12:30 PM, Verkuilen, Jay <JVerkuilen@gc.cuny.edu> wrote:
> Tomas M wrote:
>
>>I want to correct the standard errors for potential correlation between
> observations. Is there a cluster() option equivalent?  It appears that
> there is not, according to the Stata help files.<
>
> You could always bootstrap by cluster, using the bootstrap prefix
> command. I'm not aware of pitfalls that might hit in this case, but
> quantile regression isn't a "nice" smooth function of the data (as it is
> using L1 loss), so there might be problems.
>
>
>>Also, is there a command to identify the intraclass correlation
> coefficient, to determine if there is indeed clustering?  Or will it be
> okay if I bootstrap anyway, regardless?<
>
> I'm not sure if there's an equivalent or even if one makes much sense
> but one way to get a rough feel for it is to use by cluster
> bootstrapping and then bootstrap without the clustering and see how
> different things are.
>
>
>>Also, what is a typical rep number, 1000?  Or will 100 suffice?
>
> I'd say that depends on the quantile you're estimating but I'd err on
> the high side rather than the low side. How long is each replication
> taking to run? Quantile regression should be pretty fast these days but
> obviously it's not as fast as least squares.
>
> JV

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