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Predicting fixed effects in panel probit models

Author

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  • Johannes S. Kunz
  • Kevin E. Staub
  • Rainer Winkelmann

Abstract

Many applied settings in empirical economics require estimation of a large number of fixed effects, like teacher effects or location effects. In the context of binary response variables, pre-vious studies have been limited to the linear probability model, citing perfect prediction and incidental parameter biases as reasons. We explain why these problems arise and present an appropriate solution for the probit model. In contrast to other estimators, it ensures that pre- dicted fixed effects exist for all units. We illustrate the approach in simulation experiments and an application to health care utilization.

Suggested Citation

  • Johannes S. Kunz & Kevin E. Staub & Rainer Winkelmann, 2019. "Predicting fixed effects in panel probit models," Monash Economics Working Papers 10-19, Monash University, Department of Economics.
  • Handle: RePEc:mos:moswps:2019-10
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Buchmueller, Thomas C. & Cheng, Terence C. & Pham, Ngoc T.A. & Staub, Kevin E., 2021. "The effect of income-based mandates on the demand for private hospital insurance and its dynamics," Journal of Health Economics, Elsevier, vol. 75(C).
    3. Bartolucci, Francesco & Pigini, Claudia & Valentini, Francesco, 2021. "Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models," MPRA Paper 110031, University Library of Munich, Germany.

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    More about this item

    Keywords

    Perfect prediction; Incidental parameter bias; Fixed Effects; Panel data; Binary response; Bias reduction;
    All these keywords.

    JEL classification:

    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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