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Re: st: RE: RE: RE: exact matches in propensity score matching


From   Austin Nichols <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: RE: RE: exact matches in propensity score matching
Date   Fri, 22 Jun 2012 15:18:01 -0400

Yang, Yong <yongyang@essex.ac.uk>:
I would guess choice of NN, Kernel, LLR, etc. don't really matter in
your application, relative to your choice of a parametric model for
the propensity score as a logit on emp, emp2, cap, cap2.  Have you
considered dividing emp&cap into a grid at (say) quartiles (16
categories in each industry/year), and reweighting by the inverse of
the fraction exporting within cell?  This is now sometimes called
"coarsened exact matching" and has good properties relative to most
alternatives: http://www.jstor.org/stable/1555493
See also http://www.stata-journal.com/sjpdf.html?articlenum=st0136_1

On Fri, Jun 22, 2012 at 2:42 PM, Yang, Yong <yongyang@essex.ac.uk> wrote:
> Dear Daniel and Stata users,
>
> Thank you very much for these constructive suggestions.
>
> I have just seen a note on http://www.stata.com/statalist/archive/2010-09/msg00073.html . The idea seems like to add different constants to each industry year combination, and then add caliper (i.e. 0.5) when use propensity score matching to force exporter and domestic firm within the same industry and appear in the same year. Do you think it is fine to have a try on this way? if it is fine, how about followings codes to generate PMS estimate on exact matches from same industry and year?
>
>
> egen industry_year=group(industry year)
> logit exporter employees employees2 capital capital2 industry_* year_*
> predict pscore if e(sample), pr
> gen pscore2=industry_year*10+pscore
> bootstrap r(att): psmatch2 exporter, pscore(pscore2) outcome(sales) neighbor(1) caliper(0.5)
> bootstrap r(att): psmatch2 exporter, pscore(pscore2) outcome(sales) kernel bw(0.06) caliper(0.5)
> bootstrap r(att): psmatch2 exporter, pscore(pscore2) outcome(sales) radius caliper(0.5)
>
>
> Also, I am running stata on half million observations, and do you  know is there any way to speed up kernel matching estimation process? it is currently take hours to generate one estimate.
>
> Thank you very much for your time and all your help.
>
> Regards
> Yong
>
>

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