Adrian de la Garza <[email protected]>:
Clustering on t --groups of (year,quarter)-- accounts for repeated
observations in the same time period (year, quarter)... but not
serial correlation over time--across (year, quarter) groups. Cameron,
Gelbach, and Miller propose a way to allow clustering in multiple
dimensions, where you essentially estimate e(V) clustering on t, then
estimate e(V) clustering on i, then estimate e(V) clustering on t*i,
then use the sum of the first two less the last as your estimate of
e(V). If you're not worried about serial correlation, you can just
cluster on t (year,quarter). But you might try clustering on country
to see how important it looks to be (compare the SEs).
On Fri, May 9, 2008 at 5:56 PM, Adrian de la Garza
<[email protected]> wrote:
>
> Guys,
>
> Recently I posted a message about how to compute standard errors that take into account repeated observations in a same (year, quarter). I still haven't figured out whether that is an issue or not or how to solve it... but Austin suggested that I cluster by region (or country) AND year simultaneously, and he kindly sent me a link for a paper by Cameron, Gelbach, and Miller (2006) on multi-way clustering.
>
> I still haven't read the whole paper but I was wondering if you guys knew the difference between the method they propose (I found a Stata code in Doug Miller's homepage that is a bit too advanced for me) and simply defining a group variable that groups observations by country and year, and then just use the cluster(group) option when running my regression. What are the potential problems with doing the latter or how does it differ from the Cameron et al. method?
>
> Also, do you know if there's a command that implements their method in Stata?
>
> Thank you very much.
>
> Best,
> Adrian
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