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

[Sami Alameen <samialameen@gmail.com>]

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
<christina.sakali@googlemail.com> 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 <M.E.Schaffer@hw.ac.uk> 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: owner-statalist@hsphsun2.harvard.edu
>>> [mailto:owner-statalist@hsphsun2.harvard.edu] 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.
>>> *
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>>
>>
>> --
>> Heriot-Watt University is a Scottish charity
>> registered under charity number SC000278.
>>
>>
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