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Re: st: Propensity Score Matching


From   Austin Nichols <[email protected]>
To   "[email protected]" <[email protected]>
Subject   Re: st: Propensity Score Matching
Date   Tue, 11 Mar 2014 16:04:59 -0400

Tarik Demp <[email protected]>:
Better to use a true double-robust method:
 http://www.stata.com/help.cgi?teffects+ipwra
though in theory you should get approximately the same answer running
the regression in the matched data using weights _w (no guarantees in
any finite sample of course).

webuse cattaneo2, clear
ren bweight y
g mage2=mage^2
teffects ipwra (y prenatal1 mage) (mbsmoke mage mage2 medu), atet
loc b=_b[r1vs0.mbsmoke]
cap prog drop remtch
prog remtch, rclass
 tempvar u
 g `u'=uniform()
 sort `u'
 psmatch2 mbsmoke mage mage2 medu, logit neighbor(10)
 reg y prenatal1 mage mbsmoke [pw=_w]
 ret scalar atet=_b[mbsmoke]
 eret clear
end
simul, seed(1) r(300):remtch
tw kdensity atet, xli(`b')


On Tue, Mar 11, 2014 at 2:04 PM, Tarik Demp <[email protected]> wrote:
> Dear statalist,
>
> I have a basic question related to a propensity score matching. The model
> is: I have start-up companies, some of them ask their bank for a loan, some
> of them ask their family/friends. Now, I want to assess how this choice
> affect the costs of the loans. Therefore, I run the following propensity
> score matching:
>
> psmatch2 bankloan age of company industry of startup etc., neighbor(10)
> common outcome(loan costs)
>
> bankloan equals 1 if they got a loan from the bank and 0 if they got a loan
> from their family/friends. After having matched bank-financed startups
> (treated) to family-/friends-financed start-ups (untreated), I can compute
> how much more bank-financed start-ups pay compared to their peer group of
> matched family-/friends-financed start-ups.
>
> My question now is: If I would like to explain what increases the premium
> bank-financed start-ups have to pay over their comparable non-bank-financed
> startups can I use the variables I used in the probit estimation? So, could
> I use the propensity score matching just as peer selection tool and re-use
> the variables again as e.g. age of company might explain the premium
> bank-financed start-ups have to pay compared to other start-ups?
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