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

[christina sakali <christina.sakali@googlemail.com>]

Mark,

thank you, I will try the options you suggest.

And thanks again for all the other advice, warmly appreciated.

On 20 September 2011 17:55, Schaffer, Mark E <M.E.Schaffer@hw.ac.uk> wrote:
> Christina,
>
> -xtivreg2- will do this.  Or, since you have a small number of T and N fixed effects, you could add the FEs by hand.
>
> --Mark
>
>> -----Original Message-----
>> From: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
>> christina sakali
>> Sent: 20 September 2011 15:48
>> To: statalist@hsphsun2.harvard.edu
>> Subject: Re: st: RE: xtscc and small samples (equal size T and N)
>>
>> Mark,
>>
>> thank you, this is clear now.
>>
>> In the older versions of Stata,  het-robust SE were produced
>> with either - xtreg ..., fe robust- OR -xtreg ..., fe
>> vce(robust)- which both gived identical results.
>>
>> However, as far as I know these commands have changed in the
>> newer versions. Is it possible to obtain the standard
>> het-robust SE in the newer versions and how?
>>
>>
>>
>> On 20 September 2011 17:15, Schaffer, Mark E
>> <M.E.Schaffer@hw.ac.uk> wrote:
>> > Christina,
>> >
>> > The factor in front of bias term in eqn 5 is 1/(T-1).  As T
>> gets bigger, this term gets smaller.  For T=11, the bias term
>> is being multiplied by 1/10, i.e., by 0.1.
>> >
>> > -xtivreg2-, like -ivreg2-, can estimate straight OLS as
>> well as IV.  The underlying logic is GMM.  OLS, IV, FE, RE,
>> etc., are all GMM estimators.
>> >
>> > --Mark
>> >
>> >> -----Original Message-----
>> >> From: owner-statalist@hsphsun2.harvard.edu
>> >> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
>> christina
>> >> sakali
>> >> Sent: 20 September 2011 15:03
>> >> To: statalist@hsphsun2.harvard.edu
>> >> Subject: Re: st: RE: xtscc and small samples (equal size T and N)
>> >>
>> >> Mark, this is very useful information.
>> >>
>> >> Can you please clarify what exactly you mean by "the bias is
>> >> decreasing in T". To me this sounds like the bias is
>> decreasing when
>> >> T is decreasing, but then you say that T=11 may be large enough to
>> >> justify using the standard het-robust VCV, so I am not sure I
>> >> completely get what you mean.
>> >>
>> >> Also, xtivreg works with instrumental variables, will I be able to
>> >> implement it with my data?
>> >>
>> >> On 20 September 2011 16:23, Schaffer, Mark E
>> <M.E.Schaffer@hw.ac.uk>
>> >> wrote:
>> >> > Christina,
>> >> >
>> >> > With respect to your last point, you might actually be OK here.
>> >> >
>> >> > Stock & Watson show that the standard
>> >> Eicker-Huber-White-robust VCV is biased with small-N
>> large-T panels.
>> >> But if you check the paper (eqn 5), you'll see that the
>> bias term has
>> >> a 1/(T-1) in front of it.  In other words, the bias is
>> decreasing in
>> >> T.  In your case, T=11 may be enough for you to justify using the
>> >> standard het-robust VCV.
>> >> >
>> >> > There is an as-yet undocumented option in -xtivreg2-, sw,
>> >> that implements the Stock-Watson correction to the standard
>> >> het-robust VCV.  (It's still not documented because I haven't yet
>> >> verified it against a published output or another
>> >> package.)  If the sw option gives you SEs that are similar to the
>> >> standard het-robust SEs, you've got grounds to believe that T is
>> >> indeed large enough to justify using the latter.
>> >> >
>> >> > HTH,
>> >> > Mark
>> >> >
>> >> > NB: If anyone can point me to an example of Stock-Watson
>> >> SEs that I can try to replicate, I'd be most grateful.
>> >> >
>> >> > References:
>> >> >
>> >> > Stock & Watson (2008),
>> >> > http://www.princeton.edu/~mwatson/papers/ecta6489.pdf
>> >> >
>> >> >> -----Original Message-----
>> >> >> From: owner-statalist@hsphsun2.harvard.edu
>> >> >> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
>> >> christina
>> >> >> sakali
>> >> >> Sent: 20 September 2011 13:17
>> >> >> To: statalist@hsphsun2.harvard.edu
>> >> >> Subject: Re: st: RE: xtscc and small samples (equal
>> size T and N)
>> >> >>
>> >> >> Dear Gordon, thanks for the response.
>> >> >>
>> >> >> From your as well as Mark's suggestions, I get the idea
>> >> that perhaps
>> >> >> the simple two way fixed effects model is the most
>> >> appropriate choice
>> >> >> for my data, although I do understand than none of the
>> options is
>> >> >> ideal with such a small panel sample.
>> >> >>
>> >> >> In other, previous papers with similar sample sizes and
>> >> topic, I have
>> >> >> seen that they usually either go for a simple one or
>> two way fixed
>> >> >> effects model or rely on simple robust SE such as White
>> >> SE. However I
>> >> >> am aware that Stock and Watson
>> >> >> (2008) showed that these are inconsistent, so this
>> option is also
>> >> >> ruled out for my data..
>> >> >>
>> >> >> On 20 September 2011 13:29, Gordon Hughes
>> >> <G.A.Hughes@ed.ac.uk> wrote:
>> >> >> > 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
>> >> >> > g.a.hughes@ed.ac.uk
>> >> >> >
>> >> >> > =====================================
>> >> >> >
>> >> >> > Date: Tue, 20 Sep 2011 02:12:43 +0300
>> >> >> > From: christina sakali <christina.sakali@googlemail.com>
>> >> >> > 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.
>> >> >> >
>> >> >> > Christina
>> >> >> >
>> >> >> >
>> >> >> > *
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>> >> >
>> >> >
>> >> > --
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>> >> under charity
>> >> > number SC000278.
>> >> >
>> >> >
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