Feng:
What is causing the difference between linear approximations (OLS and your method) and probit is the very strong effect of x1. One way to see this is to look at the predicted probabilities for different values of x2 and x1, which you can get by using -prgen- from -spost- and create a graph of the predicted probability versus x2. You can also plot your linear approximation using the -twoway function- command and compare the two. See example code below. You can see that you have a very extreme example and that you should not trust results from OLS in this case.
HTH,
Maarten
*-----------begin example-------------
cd h:\temp
use teste.dta, clear
probit y x1 x2
prgen x2, x(x1=1) gen(x21)
prgen x2, x(x1=0) gen(x20)
reg y x1 x2
twoway line x21p1 x20p1 x21x || /*
*/ function y = _b[_cons] + _b[x1] + _b[x2]*x, range(1.3 3.7) || /*
*/ function y = _b[_cons] + _b[x2]*x, range(1.3 3.7) /*
*/ legend(label(1 "probit" "x1=1") label(2 "probit" "x1=0") /*
*/ label(3 "OLS" "x1=1") label(4 "OLS" "x1=0") )
*----------------------end example--------------------------
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting adress:
Buitenveldertselaan 3 (Metropolitan), room Z214
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
-----Original Message-----
From: [email protected] [mailto:[email protected]]On Behalf Of Feng Liu
Sent: dinsdag 18 april 2006 17:44
To: [email protected]
Subject: st: Re: RE: compute elasticity: Probit VS LPM
Usually I use Probit or Logit instaed of LPM. This time I guess the
elasticity from mfx,eyex after Probit might be wrong, so I tried LPM. Is
there a way to verify which one is right?
What I tried is:
after Probit regression,
.predict p0
.replace x2=x2*1.1
.predict p1
Then I compare p0 and p1 and find it fell by 3%. This implies the elasticity
is -0.3, which is close to the one from LPM.
*
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