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st: RE: xtscc and small samples (equal size T and N)

From   Gordon Hughes <[email protected]>
To   [email protected]
Subject   st: RE: xtscc and small samples (equal size T and N)
Date   Tue, 20 Sep 2011 11:29:15 +0100

You will probably get almost as many views about what constitutes large T and/or large N as the number of people you consult. The answer is very dependent upon the type of data which you are analysing, because panel data comes in many different forms. However, as Mark says, no one would believe that 11 gets close.

For -xtscc- you are dealing with large T asymptotics, so the reference point would be time series asymptotics. If you have annual data I doubt whether anyone would rely on large T results for T much below 30 and some might be much stricter. The problem, of course, is that many panel datasets don't meet that criterion, in which case you have to start to think carefully about what you are trying to estimate. That is the point which underlies Mark's original suggestion. Your response indicates that you may be trying to get too much out of some rather noisy - or complex - data.

Gordon Hughes
[email protected]


Date: Tue, 20 Sep 2011 02:12:43 +0300
From: christina sakali <[email protected]>
Subject: Re: st: RE: xtscc and small samples (equal size T and N)

Dear Mark, thanks a lot for the advice and recommendations.

I am a bit reluctant to go for just the simple 2-way fixed effects
model, since after implementing the necessary tests, I have found that
my residuals suffer from both heteroscedasticity and cross-sectional
dependence, so I am looking for an estimator to account for both of
these problems.

Does the inclusion of time fixed effects correct for
heteroscedasticity and/or cross-sectional dependence and how exactly
is this achieved? (or can you suggest some reference where I can find
some more information on this issue).

Can you also please clarify this for me: What is the minimum (more or
less) sample size required for the use of estimators that rely on
large T and N asymptotics?

Thank you again.


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