There are several ways to estimate standard errors with Panel Data:
1. Clustered (Rogers) Standard Errors One-way
2. Clustered Standard Errors Two-way
3. Fama-MacBeth Standard Errors
4. Newey West for panel data sets
(5. bootstrapped standard errors)
You may prefer Clustered SE one-way in the presence of unobserved individual effects (but not time effects); FM in the presence of unobserved time effects (but not individual effects); Clustered Standard Errors Two-way in the presence of both time and individual effects. Newey West perform well in the presence of unobserved individual effects but should be used as a second best.
In order to detect if the model is one-way or two-way you may perform an F-test where, supposing your e(it) = m(i) + n(t) + v(it), H0: m(1) = ... = m(I - 1) and n(1) = ... = n(T-1).
Hope this help.
Emanuele Canegrati, Ph.D.
> From: [email protected]
> To: [email protected]
> Subject: Re: RE : RE : Heteroskedasticity and fixed effects (was: st: RE: Re: Weak instruments)
> Date: Fri, 18 Jul 2008 08:05:03 -0500
>
> .
>
> This discussion reminds me of an older paper about the ttest:
>
> Homogeneity of variance in the two-sample means test by Moser and
> Stevens. The American Statistician Vol. 46, No. 1, (Feb., 1992), pp.
> 19-21.
>
> The authors looked at the practice of testing for differences in
> variance before using the Smith/Welch/Satterthwaite ttest, and also
> looked at power in the face of difference sample sizes between the two
> groups and variances.
>
> Cheers,
>
> -Dave
>
>
> On Jul 18, 2008, at 7:22 AM, Gaul� Patrick wrote:
>
>> Dear statalisters,
>>
>> I read with great interest the posts on the merits of robustfying
>> from yesterday. Thanks in particular to Mark Schaffer for
>> elaborating on my (or rather Stock and Watson's) suggestion that "In
>> practice, it just makes more sense to always use robust standard
>> errors [rather than the usual standard errors]".
>>
>> I routinely use robust standard errors rather than the the usual
>> standard errors and the arguments raised yesterday did not really
>> convince me that this might not be a good idea. If I recap the
>> arguments as I understood them:
>>
>> a) robustifying will not help if the model is misspecified.
>>
>> Certainly, but then neither will the use of the usual standard errors.
>>
>> b) robustifying might result in losing power, particularly in small
>> and medium samples.
>>
>> Sure, but if there is heteroskedasticity the usual standard errors
>> will be inconsistent. So this suggests that some other ways to
>> address heteroskedasticity should be explored, not that the usual
>> standard errors should be used. If there is homoskedasticity, then I
>> indeed would be better off with the usual standard errors but I
>> suspect that homoskedasticity is the exception rather than the rule
>> and that heteroskedasticity is much more prevalent in practice.
>>
>> c) if the model is correctly specified, then robustifying makes very
>> little difference.
>>
>> Perhaps, but that's hardly an argument for not using robust standard
>> errors.
>>
>> Patrick Gaul�
>>
>> *
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>
>
> *
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