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st: Re: RE: compute elasticity: Probit VS LPM

From   "Feng Liu" <[email protected]>
To   <[email protected]>
Subject   st: Re: RE: compute elasticity: Probit VS LPM
Date   Tue, 18 Apr 2006 11:43:31 -0400

Thank you Maarten.

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.

Any other suggestions? The data is available at which is only 12KB.

Thanks again,


----- Original Message -----
From: "Maarten Buis" <[email protected]>
To: <[email protected]>
Sent: Tuesday, April 18, 2006 10:33 AM
Subject: st: RE: compute elasticity: Probit VS LPM

> Feng:
> 1) The linear probability model was used in a time that maximum likelihood
was too computationally burdensome. This is no longer an excuse for using
the lpm, in a time when the average coffee maker has more computing power
than a super computer of that long gone era.
> 2) The lpm should be a reasonable approximation of -probit- as long as the
probabilities of success for given values of x1 and x2 remain between .2 and
.8. Outside that range the linearity usually breaks down, and OLS is no
longer is a good approximation of -probit- (or -logit-). Anyhow, why settle
for an approximation when the real thing is easy to obtain?
> HTH,
> Maarten
> -----------------------------------------
> 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
> -----------------------------------------
> -----Original Message-----
> From: [email protected]
[mailto:[email protected]]On Behalf Of Feng Liu
> Sent: dinsdag 18 april 2006 16:13
> To: [email protected]
> Subject: st: compute elasticity: Probit VS LPM
> I run a simple model: y = x1 + x2. Both y and x1 are dummy variables and
> they are highly positively correlated. x2 is a continuous variable. I use
> command mfx, eyex to estimate the elasticity of x2. I tried both Probit
> OLS. However, the elasticities from them are much different. The one from
> Probit is -1.27 and the one from OLS is -0.27. I wonder what might be the
> problem.
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