Dear Eva and Garry,
Thanks very much for your help. Would that imply by any means that I
should avoid clustering in my case? Naively, it seems like I am
shrinking my sample from 304 to only 9 observations. Hope you can
comment on this.
Regards,
Erasmo
On Wed, Apr 15, 2009 at 11:34 AM, Eva Poen <[email protected]> wrote:
> Erasmo,
>
> this depends on the degrees of freedom. With clustering, your df can
> be greatly reduced. If, for example, you have 7 degrees of freedom
> (i.e. 8 industries), the p-value will be equal to 2*ttail(7,1.78)
> which is around 0.118.
>
> Hope this helps,
> Eva
>
>
>
> 2009/4/15 Erasmo Giambona <[email protected]>:
>> Dear Statlist,
>>
>> I am fitting a model to a cross-sectional data set of 304 firms across
>> 9 industries. I fit the model using regress with the robust and
>> cluster options (which I use to cluster standard errors at the
>> industry level). One of the variables obtains a "t" of 1.78 but its
>> p-value is "only" 0.114. Shouldn't the p-value be lower than 10% in
>> this case?
>>
>> I really cannot explain this.
>>
>> I hope somebody could provide an explanation.
>>
>> Thanks,
>>
>> Erasmo
>> *
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>>
>
> *
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>
*
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