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Testing the binomial fixed effects logit model; with an application to female labor supply

Author

Listed:
  • Rainer Winkelmann
  • Lin Xu
Abstract
Regression models for proportions are frequently encountered in applied work. The conditional expectation function is bounded between 0 and 1 and therefore must be non-linear, requiring nonstandard panel data extensions. One possible approach is the binomial panel logit model with fixed effects (Machado, 2004). We propose a new and simple implementation of the conditional maximum likelihood estimator for standard software. We investigate the properties of the estimator under misspecification and derive a new test for overdispersion. Estimator and test are applied in a study of contracted working volumes, measured as proportion of full-time work, for women in Switzerland.

Suggested Citation

  • Rainer Winkelmann & Lin Xu, 2019. "Testing the binomial fixed effects logit model; with an application to female labor supply," ECON - Working Papers 321, Department of Economics - University of Zurich, revised Oct 2019.
  • Handle: RePEc:zur:econwp:321
    as

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    File URL: https://www.zora.uzh.ch/id/eprint/170370/7/econwp321.pdf
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    References listed on IDEAS

    as
    1. Kunz, J.S.; & Staub, K.E.; & Winkelmann, R.;, 2018. "Predicting fixed effects in panel probit models," Health, Econometrics and Data Group (HEDG) Working Papers 18/23, HEDG, c/o Department of Economics, University of York.
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    6. Machado, Matilde P., 2004. "A consistent estimator for the binomial distribution in the presence of "incidental parameters": an application to patent data," Journal of Econometrics, Elsevier, vol. 119(1), pages 73-98, March.
    7. Gordon Kemp & João Santos Silva, 2016. "Partial effects in fixed-effects models," United Kingdom Stata Users' Group Meetings 2016 06, Stata Users Group.
    8. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, September.
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    More about this item

    Keywords

    Proportions data; unobserved heterogeneity; conditional maximum likelihood; overdispersion;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

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