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Re: st: RE: xtscc and small samples (equal size T and N)
From
christina sakali <[email protected]>
To
[email protected]
Subject
Re: st: RE: xtscc and small samples (equal size T and N)
Date
Tue, 20 Sep 2011 02:12:43 +0300
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
On 20 September 2011 00:50, Schaffer, Mark E <[email protected]> wrote:
> Christina,
>
> FWIW, I'd be reluctant to follow Sami's advice, for the same reasons I gave earlier, and in spite of my soft spot for recommendations involving -ivreg2-. Two-way clustering requires asymptotics where both T->infinity and N->infinity. 11 is not very far on the way to infinity no matter how you slice it.
>
> What about a simple 2-way fixed effects model with both group fixed effects and time dummies? You have 121 observations, and you're losing 22 dofs to the FEs, so it's not tooooo bad....
>
> --Mark
>
>> -----Original Message-----
>> From: [email protected]
>> [mailto:[email protected]] On Behalf Of
>> Sami Alameen
>> Sent: 19 September 2011 19:46
>> To: [email protected]
>> Subject: Re: st: RE: xtscc and small samples (equal size T and N)
>>
>> It's up to you but I would use -ivreg2- with two-way
>> clustering as follow:
>>
>> ssc install ivreg2, replace
>>
>> use grunfeld
>>
>> xi, noomit: ivreg2 invest kstock mvalue i.company, noconst
>> cluster(company year)
>>
>> And igore the irrelevant segments of the output!
>>
>> Sami
>>
>> On Mon, Sep 19, 2011 at 8:24 PM, christina sakali
>> <[email protected]> wrote:
>> > Dear Mark, thanks for the response.
>> >
>> > The first two specifications differ only in respect to one
>> explanatory
>> > variable, while the third specification includes both these two
>> > variables from the previous two specifications.
>> >
>> > After estimating them with xtreg ..., fe, I checked for serial and
>> > cross-sectional correlation (using -xtregar, ... fe lbi- and xtcsd).
>> > The results indicated NO serial correlation, but the presence of
>> > cross-sectional dependence.
>> >
>> > Moreover, I read in Hoechle (SJ, 2007, p.17) that the
>> Driscoll-Kraay
>> > SE have better small sample properties than other more commonly
>> > employed estimators when cross-sectional dependence is
>> present, that
>> > is why I chose to estimate my model with xtscc.
>> >
>> > If both xtscc and cluster are not appropriate for a small
>> sample like
>> > mine, then what is the appropriate estimator, when one needs to
>> > account for the presence of cross-sectional dependence? Or should I
>> > just use -xtreg, ... fe robust-, which only accounts for
>> > heteroscedasticity?
>> >
>> > Any suggestions are greatly appreciated.
>> >
>> > On 19 September 2011 19:38, Schaffer, Mark E
>> <[email protected]> wrote:
>> >> Christina,
>> >>
>> >> You don't tell us how the 3 specifications differ. It's hard to
>> >> offer explanations for the differences in results without
>> this information.
>> >>
>> >> That said, it looks like you have a basic problem here.
>> >>
>> >> The cluster-robust approach gives you SEs that are robust to
>> >> arbitrary within-group autocorrelation. It relies on
>> asymptotics in
>> >> which the number of clusters N goes off to infinity. 11
>> is not very
>> >> far on the way to infinity.
>> >>
>> >> The Driscoll-Kraay SEs implemented by -xtscc- apply the
>> kernel-robust
>> >> approach (e.g., Newey-West) to panel data. It gives you
>> SEs that are
>> >> robust to arbitrary common (across-groups) autocorrelated
>> disturbances.
>> >> This approach relies on asymptotics in which the number of
>> >> observations in the T dimension goes off to infinity. 11
>> is not very
>> >> far on the way to infinity.
>> >>
>> >> Personally, I'd be reluctant to use either of these
>> approaches with
>> >> an
>> >> N=11/T=11 panel. Maybe others on the list can offer some
>> suggestions
>> >> for alternatives.
>> >>
>> >> Sorry to sound so negative, but that's how it looks from here.
>> >>
>> >> --Mark
>> >>
>> >>> -----Original Message-----
>> >>> From: [email protected]
>> >>> [mailto:[email protected]] On Behalf
>> Of christina
>> >>> sakali
>> >>> Sent: 19 September 2011 12:44
>> >>> To: statalist
>> >>> Subject: st: xtscc and small samples (equal size T and N)
>> >>>
>> >>> Hello all,
>> >>>
>> >>> I am estimating 3 different specifications of a panel
>> fixed effects
>> >>> model with T=N=11. According to Pesaran's test I have found the
>> >>> presence of contemporaneous correlation in all 3 specifications.
>> >>>
>> >>> I then tried to estimate all 3 specs with both -xtscc ...,
>> >>> fe- and -xtreg ..., fe cluster(panelvar) -
>> >>>
>> >>> When comparing the S.E. produced by the two estimators, I was
>> >>> surprised to notice the following:
>> >>>
>> >>> Although in the first spec, xtscc S.E. were ALL larger
>> than cluster
>> >>> S.E., in the other two specs xtscc S.E. were either larger or
>> >>> smaller than cluster S.E. However the difference was rather small.
>> >>>
>> >>> What does this indicate for my data and model (when xtscc
>> produces
>> >>> both smaller and larger S.E. than cluster in the same
>> specification)
>> >>> and which of the two estimates (xtscc or
>> >>> cluster) should I trust as more appropriate for my model?
>> >>>
>> >>> I am using Stata 9.2.
>> >>>
>> >>> Any help or suggestions are appreciated.
>> >>> *
>> >>> * 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/
>> >
>>
>> *
>> * 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/