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Re: st: RE: clustered standard errors
From
Robert Lineira <[email protected]>
To
[email protected]
Subject
Re: st: RE: clustered standard errors
Date
Thu, 29 Apr 2010 17:46:50 +0200
Dear,
The population are the 17 Spanish regions and the samples are
post-election surveys in each region. The purpose of the analysis is to
look for variances on the strength of local and national forces on
voting and turnout.
Although the multi-stage sampling procedure takes advantage of some
strata and clusters to select the individuals, the samples may be
considered as random samples of voters in each region. The pool of
samples consists in the aggregation of this random samples.
I hope this helps in having a better idea of the research.
Thanks in advance!
Robert.
Al 29/04/2010 14:06, En/na Steve Samuels ha escrit:
I wonder what the purpose of the analysis is, what the sampled
populations are, and what the sample designs are. Survey samples can
be complex creations with their own strata and clusters. Until Robert
provides more detail, I'm not sure that 1 sample = 1 cluster.
Steve
Steve
On Thu, Apr 29, 2010 at 6:03 AM, Schaffer, Mark E<[email protected]> wrote:
Robert,
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of
Robert Lineira
Sent: 29 April 2010 10:08
To: [email protected]
Subject: st: clustered standard errors
Dear all,
I found on the net a presentation by Austin Nichols and Mark
Schaffer on the net on clustered standard errors. After
reading it, some questions emerged to me on how to use them.
I want to run an analysis using a pool of 17 survey samples.
Supposedly, standard errors will be correlated within the
clusters, but the presentation advises that to use clustered
standard error might be a very bad solution. They suggest to
perform some test before using the corrected errors running
'cltest' and 'xtcltest' stata commands.
Unfortunately, I just found 'cltest' command, I am not sure
is the same they use given that is previous to the Kédzi
(2007) paper they quote.
No, that's a different test. The test code Austin and I referred to in our presentation is still languishing in alpha testing.
But I'm not sure it or other tests can help you.
The problem is that this test, like White's general heteroskedasticity test and related tests, works via a vector-of-contrasts. The contrast is between the elements of the robust and non-robust VCVs.
Under the null, the robust VCV is consistent. If the non-robust VCV is also consistent, its elements will be similar to those of the robust VCV, and the vector of contrasts will be small. If the non-robust VCV is inconsistent, the contrast will be large.
You can see the problem now. To do this or a related test in your application, you need a robust VCV that is consistent. Your cluster-robust VCV is indeed consistent, but with only 17 clusters, you are not very far along the way to infinity, and it's likely to be a poor estimator of the VCV. Contrasting it with the non-robust VCV is not going to give you a reliable test - the contrast could be big because the cluster-robust VCV is poor, for example.
Hope this helps.
Cheers,
Mark
My question is if anyone knows a test which I could use
before applying clustered standard errors and (if not) which
solution do you find better in a case such as this.
Regards
Robert.
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