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st: Inverse Mills in clustered (multilevel) cross-sectional panel data

From   Erkko Autio <[email protected]>
To   <[email protected]>
Subject   st: Inverse Mills in clustered (multilevel) cross-sectional panel data
Date   Mon, 7 Sep 2009 16:41:14 +0100

I am trying to use inverse Mill's ratio to control for self-selection in clustered data.

My problem is that I have multilevel data, clustered by year and country. The basic dataset comprises interviews with some 900 000 individuals in nearly 60 countries over 10 years.

Specifically, I am assuming that self-selection of individuals into a given economic activity is influenced by both individual-level variables (such as age, gender, attitudes), as well as country-level variables (e.g., taxation). As behaviours may be conditioned by context (the same individual would behave differently under different taxation regimes, for example), the error terms will no longer be normally distributed, and techniques assuming normal distribution of error terms will thus give biased estimates.

My selection equation would thus consist of both individual and country-level variables, as would my regression equation.

Hence my problem. Normally, inverse Mill's ratio is computed using probit models. However, Stata has no multi-level probit. It does have xtmelogit, which can be used for multi-level data. If I use xtmelogit instead of, say, xtprobit to compute inverse Mill's ratio? Will xtmelogit result in biased estimates?

Alternatively, is there a way to do a probit in cross-sectional panel data (i.e., one-off interviews of randomly sampled individuals in lots of countries in many consequtive years, individuals not followed over time)?

Thank you for any suggestions.

Erkko Autio

Erkko Autio, Professor
Imperial College Business School
Tanaka Building
South Kensington Campus
London SW7 2AZ, UK
E: [email protected]

fn:Erkko Autio
org:Imperial College;Imperial College Business School
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email;internet:[email protected]
tel;work:+44 207 594 1991
tel;cell:+44 77 8622 6452

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