Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down at the end of May, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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


From   christina sakali <christina.sakali@googlemail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: xtscc and small samples (equal size T and N)
Date   Tue, 20 Sep 2011 17:02:41 +0300

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
>> >
>> >
>> > *
>> > *   For searches and help try:
>> > *   http://www.stata.com/help.cgi?search
>> > *   http://www.stata.com/support/statalist/faq
>> > *   http://www.ats.ucla.edu/stat/stata/
>> >
>>
>> *
>> *   For searches and help try:
>> *   http://www.stata.com/help.cgi?search
>> *   http://www.stata.com/support/statalist/faq
>> *   http://www.ats.ucla.edu/stat/stata/
>>
>
>
> --
> Heriot-Watt University is a Scottish charity
> registered under charity number SC000278.
>
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index