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st: Re: selection bias in panel data

From   "Scott Merryman" <>
To   <>
Subject   st: Re: selection bias in panel data
Date   Tue, 16 Sep 2003 20:10:15 -0500

----- Original Message ----- 
From: "Rodrigo Taborda" <>
To: <>
Sent: Tuesday, September 16, 2003 5:48 PM
Subject: st: selection bias in panel data

> Hello
> Does Stata have an order or procedure to perform heckman selection bias
> in panel data?
> Tnak you
> -- 

Last month, "tom blade" <> asked a similar question.  I
made the following suggestion:

A couple of resources to take a look at:

1.  Wooldrige (Econometric Analysis of Cross Section and Panel Data) section
17.7 discusses sample selection in panel models.

2.  At the combined Dutch and German Stata users meeting 2002, Sophia
Rabe-Hesketh ( )presented "Multilevel selection models
using GLLAMM"   The slides of the presentation are available at:

Models for handling sample selection or informative missingness have been
developed for both cross-sectional and longitudinal or panel data. For
cross-sectional data, Heckman (1979) suggested a joint model for the response
and sample selection processes where the disturbances of the processes are
correlated. For longitudinal data, Hausman and Wise (1979) and Diggle and
Kenward (1994) developed a model in which the continuous response (observed or
unobserved), and possibly the lagged response, is a predictor of attrition or
dropout. The Heckman model can be estimated using the heckman command in Stata
and the Diggle-Kenward model is available in the Oswald package running in
S-PLUS. Both models can also be estimated using gllamm with the advantage that
the following three generalisations are possible. First, the models can be
extended to multilevel settings where there may be unobserved heterogeneity
between the clusters at the different levels in both the substantive and
selection processes and where selection may operate at several levels. Second,
the Heckman model can be modified for nonnormal response processes. Third, both
the Heckman and Diggle-Kenward models can be extended to situations where the
substantive response is a latent variable measured by a number of indicators. I
will show how the standard Heckman and Diggle-Kenward models are estimated in
gllamm and give a examples of all three types of generalisation of these
standard models. The research was carried out jointly with Anders Skrondal and
Andrew Pickles. "

Hope this helps,

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