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From |
Austin Nichols <[email protected]> |

To |
[email protected] |

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

Date |
Mon, 7 Sep 2009 16:51:59 -0400 |

Erkko Autio<[email protected]> : A lot of techniques assuming normal distribution of error terms will give biased estimates, but that doesn't stop folks from using them--however, if you want an IMR you are asking for normal densities and cumulative probabilities. Plus maybe your theoretical model demands interactions between various groups of variables. Probably you were looking for a dynamic selection model, none of which is easily estimated in Stata afaik; see e.g. 8.6.2 in http://www.stata.com/bookstore/aopd.html or 17.7.2 in http://www.stata.com/bookstore/cspd.html or references in this thread: http://www.stata.com/statalist/archive/2005-06/msg00456.html and see what kind of model you need to program for your case. Or switch to panel IV using -xtivreg- if you can--sounds like you've got the sample size for it! On Mon, Sep 7, 2009 at 11:41 AM, Erkko Autio<[email protected]> wrote: > 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 > - * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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