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| From | "Jost Heckemeyer" <Heckemeyer@zew.de> |
| To | <statalist@hsphsun2.harvard.edu> |
| Subject | Antw: Re: st: Clustered standard errors: Insufficient observations |
| Date | Sat, 14 May 2011 20:45:33 +0200 |
The "generally recommened" referred to the example I gave, i.e. I have cross-country data where several 100s and 1000s of firms are clustered within several dozens of countries, so clustering and the country level would indeed make sense, as you write it, Stas. Still, it does not work although and I cannot see any reason why it doesn t. >>> Stas Kolenikov 14.05.11 14.22 Uhr >>> On Sat, May 14, 2011 at 12:30 PM, Jost Heckemeyer wrote: > Dear Statalisters, > I want to estimate a large cross-country panel model (> 50.000 firm year > observations). As some of my main explanatory variables vary mainly at > the country-level (e.g. tax rates) I cluster standard errors within > countries, not within firms - as it is generally recommended to do. > > However, as soon as I estimate a firm fixed effects model (xtreg, fe > with option cluster(country), fe are at the firm level) it does not work > anymore and it just gives me the error "insufficient observations". > xtreg, re and all pooled estimators all work well with cluster(country). > xtreg, fe also works with clustering within firms. But this is not what > I want. It would be great if anyone coule help me with this problem. > What can I do? Clustered standard errors are intended to work at the highest level of your sampling, or the highest level at which you expect correlations in the error terms (because of unobserved or omitted variables, say). I'd be curious to see as to who "generally recommends" clustering at the level at which the explanatory variables vary; if this were a good claim, you would have to cluster on gender in any labor market regression, leaving you with 2 d.f.s to estimate at most 1 parameter besides the intercept. If you sampled 3 countries from a list of developing countries, and then 10K firms within country, then you would want to cluster at the level of the countries (although it won't produce reasonable results, since you need at least several dozen clusters to get sensible performance). If you had three countries because that's where your collaborators have been, the country dimension has nothing to do with sampling, and the legitimate sampling units would be firms (unless of course you sampled industry codes first, in which case you would need to cluster by the industry codes). If you are concerned about lack of control over your country dimension, you could specify interactions of your explanatory variables (may be a subset of them) with the country variable using something like i.country#(c.employment i.ownership). -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ Zentrum für Europäische Wirtschaftsforschung GmbH (ZEW), L7,1 68161 Mannheim Sitz der Gesellschaft: Mannheim Amtsgericht Mannheim HRB 6554 Aufsichtsratsvorsitzender: Gerhard Stratthaus MdL, Finanzminister a.D. Geschaeftsfuehrer: Prof. Dr. Dr. h.c. mult. Wolfgang Franz, Thomas Kohl Centre for European Economic Research L7,1 68161 Mannheim Germany Seat of the Company: Mannheim Local Court Mannheim HRB 6554 Chairman of the Supervisory Board: Gerhard Stratthaus MdL, Minister, ret. Executive Directors: Prof. Dr. Dr. h.c. mult. Wolfgang Franz, Thomas Kohl -------------------------------------------------------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/