st: weighting regressions and clustering standard errors

[Zeke Hausfather <zeke@efficiency20.com>]

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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(m) 917 520 9601

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