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| From | Austin Nichols <austinnichols@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: weights |
| Date | Wed, 15 May 2013 07:27:18 -0400 |
Hsu-Chih <simon.cheng@uconn.edu> and Nick: I think one can go farther and correct at least one misapprehension before starting with a long list of readings. Unweighted regression is preferred not "because it is less biased and more consistent than the weighted analysis" but because in a *correctly specified model* it has lower variance (i.e. we start with the assumption of no bias and then show that unweighted regression has lower variance, implying lower mean squared error). Models are rarely if ever correctly specified in the real world, outside of a simulation, and so the question is one of trading off a loss of efficiency when weighting against potential reductions in bias, a very complex tradeoff. Many economists are comfortable assuming their model is correctly specified and ignoring weights, but I am not one of them. I would recommend never ignoring the weights. On Tue, May 14, 2013 at 6:27 PM, Nick Cox <njcoxstata@gmail.com> wrote: > Your general questions on weights are best addressed by starting with > the sections on weights in [U] and then looking at the [SVY] manual: > these manuals are included as .pdf documents with modern versions of > Stata. > > I doubt that even sampling experts on this list (I am not one) can > make authoritative statements about your data from what you tell us. > > Please do read http://www.stata.com/support/faqs/resources/statalist-faq/#others > before your next post. > Nick > njcoxstata@gmail.com > > > On 14 May 2013 23:15, Cheng, Hsu-Chih <simon.cheng@uconn.edu> wrote: >> Dear Statalist veterans, >> >> Stata offers four options of weights: frequency weights, analytic weights, sampling weights, and importance weights. Winship and Radbill (1994) suggest that un-weighted regression is preferred because it is less biased and more consistent than the weighted analysis, but their discussion is applicable only to sampling weights. If the values of weights in my data center around 1 (some values are smaller than 1; some are greater), is it possible that these are still sampling weights? Or, if the weights in the data are analytic or importance weights, what are the properties of using these weights in regression analysis? Any suggestion or direction to read more on this issue is highly appreciate. >> >> Best, >> >> Simon * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/