RE: st: Mime-Version: 1.0

["Jun Xu" <mystata@hotmail.com>]

in heckman models, you need to correlate errors across two equations, and 
because the error terms for both probit and regression are normally 
distributed, it's pretty easy to derive bivariate normal, and easy for 
maximum likelihood estimation. For logit, might not be that easy for ml. I 
am not sure what about two-step procedure, I think logit might work, but you 
need to derive mills ratio first and then stick it into regression. However, 
it is inefficient. For details, you need to read Madalla 1983, which I think 
is relatively accessible.

Jun Xu
Department of Sociology
Indiana University


> From: Jacqueline Fernandez <lfjacq@usm.my>
> Reply-To: statalist@hsphsun2.harvard.edu
> To: statalist@hsphsun2.harvard.edu
> Subject: st: Mime-Version: 1.0
> Date: Mon, 04 Apr 2005 13:13:53 +0800
>
> I am using STATA for my PhD research. In my work, I want to estimate the
> earnings function of women but I want to correct for sample selectivity
> bias using the Heckman procedure which involves estimating a labour force
> participation function. The inverse Mills ratio which is derived from the
> participation function and then included in the earnings function. In
> estimating the labour force participation function, do I use a probit or
> logit model? Is it preferable to use probit as most researches do and why
> is probit prefered in the Heckman procedure?
>
> Jacqueline Fernandez
>
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