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From |
Divya Balasubramaniam <divya@uga.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: When number of regressors greaterthan the number of clusters in OLS regression |

Date |
Mon, 1 Sep 2008 21:08:15 -0400 (EDT) |

Thank you all for your invaluable suggestions. I really appreciate it. Divya. ---- Original message ---- >Date: Mon, 1 Sep 2008 19:19:48 -0400 >From: Steven Samuels <sjhsamuels@earthlink.net> >Subject: Re: st: When number of regressors greater than the number of clusters in OLS regression >To: statalist@hsphsun2.harvard.edu > >Thanks Mark. I've been thinking that the data were not *sampled* as >clusters. Since they were not, I erroneously assumed that there would >not be cluster effects. I agree clustered effects should be >considered. As Vince Wiggins stated in http://www.stata.com/statalist/ >archive/2005-10/msg00594.html , "We can use the [robust] covariance >matrix to test any subset of joint hypotheses that does not exceed >its rank." Thus Divya can get valid standard errors for single >coefficients, if she adds states as clusters, and can probably make >most of the inferences she is interested in. > >-xtreg- offers some intriguing possibilities, for it would >distinguish between state-level and district-level predictors of the >same kind. Of course statistics from neighboring districts may be >spatially correlated, opening up a completely different area of >analysis. > >Perhaps the best advice to Divya that I can give, in addition to Mark's: > >Clarify your purpose--is the study exploratory ("find a good >predictive model")? Or are you testing hypotheses about certain >predictors? If your analysis is exploratory, consider holding out a >random set of districts or states on which to test the fit of your >"best" models. If you are interested in certain predictors, than >others are potential effect modifiers and confounders. You probably >don't need them all. Do you have 25 predictors because you know they >are all important from other studies? The more unnecessary >predictors you have in one model, the more difficult it will be to >tease out the truly important ones. > >-Steve > > > >On Sep 1, 2008, at 6:00 PM, Schaffer, Mark E wrote: > >> >> Whether or not you need to use cluster-robust depends on whether you >> think your data have a problem that cluster-robust can address, namely >> (1) the error terms in your equation are correlated within states >> because of unobserved heterogeneity (so the iid assumption fails), but >> (2) the error terms are not correlated across states. >> >> A good example would be whether you are looking at something that is >> affected by state-level regulation, i.e., the laws regulating it vary >> from state to state, but you don't have variables that control for >> this >> somehow. > >* >* 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/ ======================================= Divya Balasubramaniam Economics PhD Student Terry College of Business University of Georgia Athens -30602. * * 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/

**Follow-Ups**:**RE: st: When number of regressors greater than the number of clusters in OLS regression***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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