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Re: st: Small sample with clustered data

Subject   Re: st: Small sample with clustered data
Date   Mon, 28 Nov 2011 22:14:25 +0100 (CET)

Dear Austin,

thank you for your reply. If I understand you correct,
you suggest to use cluster(countryid) after the regression, while
controlling for euclus. Countryid is a number from 1 to 50. This works.
The results are the same as if I use the robust option after the regression.
So do you think this is the best option and I should state that SE are
probably biased downward and thus significant results have to be interpreted with caution?
What if the coefficients are still significant even though I do not use the cluster option? Is there a way
to estimate the bias?



-----Ursprüngliche Nachricht-----
Von: "Austin Nichols" <>
Gesendet: 28.11.2011 20:00:41
Betreff: Re: st: Small sample with clustered data

>Lars <>:
>You are likely to have SEs biased downward no matter what you do, if
>you use the 24 cluster design--can you cluster by country (50
>clusters) but include eucluster as an explanatory variable?
>On Mon, Nov 28, 2011 at 6:24 AM, <> wrote:
>> Dear Statalist,
>> My sample consists of 50 countries with 26 of them being EU Member States.
>> The problem is that the values of the dependent variable for the EU members are not
>> independent of each other. Thus, I created a dummy variable "eucluster" that indicates
>> if a country is in the EU (1=yes; 0=no) and used the cluster(eucluster) option after the
>> OLS Regressions in Stata 10. However, in "Clustered Errors in Stata"
>> (Nichols/Schaffer 2007 - it is mentioned that if M,
>> the number of clusters, is small matters could even get worse by using the cluster option (Sheet 20).
>> M=50 seems to be the minimum number of clusters required.
>> I have 24 clusters consisting of 1 country and 1 cluster comprising 26 EU members (6 independent variables).
>> I do not know how to deal "correctly" with these clustered data in Stata. Hence, I would highly appreciate if someone could
>> give me advice or suggest a solution on how to deal with the clustered data in such a small sample.
>> Thanks for Consideration.
>> Lars
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