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From |
"Nuno" <liststata@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: Re: st: Two-way clustering |

Date |
Sun, 11 May 2008 12:22:43 +0100 |

Hi everyone, I'm sorry to hijack this discussion, but I have a problem that relates to this question. When using the two way clustering we can still have problems with OLS estimates if residuals are correlated within cluster. The consequence of this would be that OLS standard errors would be biased but also the slope coefficient is not efficient. In order to minimize this problem we should use GLS using the - xtreg - command. My problem is the following: how can we test the for residuals serial correlation within cluster? I have no problem with testing the serial correlation without clustering but I'm unsure of how to do this when the original model used clustered standard errors... Another issue would be if GLS are used when no serial correlation is present, would the estimated standard errors be biased? At first glance I don't think so, but I'm not sure about this. Best, Nuno -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of nicola.baldini2@unibo.it Sent: 11 May 2008 10:02 To: statalist@hsphsun2.harvard.edu Cc: kokootchke@hotmail.com Subject: Re: Re: st: Two-way clustering I downloaded the Stata program to estimate a linear model with two-way clustering from somewhere around http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se by using the method in the reference you cited. It is quite easy to use: creg depvar [indepvars] [if] [in] , fcluster(varname) tcluster(varname) [bcluster(integer) where fcluster(varname) adjusts standard errors for clustering on e.g. the firm variable tcluster(varname) adjusts standard errors for clustering on e.g. the time variable and finally bcluster(integer) is needed if there are multiple observations per firm-year. 0 is default value Nicola At 02.33 10/05/2008 -0400, you wrote: >Adrian de la Garza <kokootchke@hotmail.com>: >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 ><kokootchke@hotmail.com> 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 * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: Re: st: Two-way clustering***From:*nicola.baldini2@unibo.it

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