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Almost Consistent Estimation of Panel Probit Models with 'Small' Fixed Effects

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  • Michael Lechner
  • Francois Laisney

Abstract

We propose four different GMM estimators that allow almost consistent estimation of the structural parameters of panel probit models with fixed effects for the case of small T and large N. The moments used are derived for each period from a first order approximation of the mean of the dependent variable conditional on explanatory variables and on the fixed effect. The estimators differ w.r.t. the choice of instruments and whether they use trimming to reduce the bias or not. In a Monte Carlo study we compare these estimators with pooled probit and conditional logit estimators for different DGPs. The results show that the proposed estimators outperform these competitors in several situations.

Suggested Citation

  • Michael Lechner & Francois Laisney, 2002. "Almost Consistent Estimation of Panel Probit Models with 'Small' Fixed Effects," University of St. Gallen Department of Economics working paper series 2002 2002-15, Department of Economics, University of St. Gallen.
    • Handle: RePEc:usg:dp2002:2002-15
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    1. Avery, Robert B & Hansen, Lars Peter & Hotz, V Joseph, 1983. "Multiperiod Probit Models and Orthogonality Condition Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 24(1), pages 21-35, February.
    2. Thierry Magnac, 2002. "Binary variables and fixed effects: generalizing conditional logit," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 D6-4, International Conferences on Panel Data.
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    11. Bertschek, Irene & Lechner, Michael, 1998. "Convenient estimators for the panel probit model," Journal of Econometrics, Elsevier, vol. 87(2), pages 329-371, September.
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    Cited by:

    1. Eric A. Posner & Miguel F. P. de Figueiredo, 2005. "Is the International Court of Justice Biased?," The Journal of Legal Studies, University of Chicago Press, vol. 34(2), pages 599-630, June.
    2. Lechner, Michael & Lollivier, Stefan & Magnac, Thierry, 2005. "Parametric Binary Choice Models," IDEI Working Papers 398, Institut d'Économie Industrielle (IDEI), Toulouse.
    3. Anders Skrondal & Sophia Rabe-Hesketh, 2022. "The Role of Conditional Likelihoods in Latent Variable Modeling," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 799-834, September.

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    More about this item

    Keywords

    Panel data; binary choice model; generalised method of moments; fixed effects;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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