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Re: st: weighting regressions and clustering standard errors
From
Zeke Hausfather <[email protected]>
To
[email protected]
Subject
Re: st: weighting regressions and clustering standard errors
Date
Fri, 25 Mar 2011 11:14:16 -0700
Nevermind, I just realized that the problem was due to using iweight
(which doesn't play well with cluster) instead of fweight, which seems
to work fine.
On Fri, Mar 25, 2011 at 11:06 AM, Zeke Hausfather <[email protected]> wrote:
> I'm working on an analysis of the difference in trends between urban
> and rural temperature stations in the U.S. Specifically, I'm taking
> all permutations of urban and rural station pairs that fulfill certain
> requirements (e.g. are relatively close to each other, have the same
> instrument type, etc.). This results in a list of station pairs that,
> while unique, can include many permutations of the same urban station
> being paired with multiple nearby rural stations and vis versa.
>
> I'm trying to regress the difference between the urban and rural
> stations in the pairs against a time variable (months) in a way that
> avoids overweighting cases where a spatial cluster of stations results
> in numerous permutations of the same urban or rural stations. I've
> tried using the cluster command, which gives me a better estimate of
> the standard errors given the non-unique data points, but this
> prevents me from also applying a weight to the regression based on the
> relative frequency of station occurrence in the pairs. I'm curious if
> anyone has thoughts on the best way to estimate the unique
> occurrence-weighted OLS fit while also reflecting this correctly in
> the standard errors.
>
> --
> Zeke Hausfather
> Chief Scientist
> Efficiency 2.0
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