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Convenient estimators for the panel probit model: Further results


  • William Greene


Bertschek and Lechner (1998) propose several variants of a GMM estimator based on the period specific regression functions for the panel probit model. The analysis is motivated by the complexity of maximum likelihood estimation and the possibly excessive amount of time involved in maximum simulated likelihood estimation. But, for applications of the size considered in their study, full likelihood estimation is actually straightforward, and resort to GMM estimation for convenience is unnecessary. In this note, we reconsider maximum likelihood based estimation of their panel probit model then examine some extensions which can exploit the heterogeneity contained in their panel data set. Empirical results are obtained using the data set employed in the earlier study. Copyright Springer-Verlag 2004
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  • William Greene, 2002. "Convenient estimators for the panel probit model: Further results," Working Papers 02-06, New York University, Leonard N. Stern School of Business, Department of Economics.
  • Handle: RePEc:ste:nystbu:02-06

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    9. Moura, Guilherme V. & Richard, Jean-François & Liesenfeld, Roman, 2007. "Dynamic Panel Probit Models for Current Account Reversals and their Efficient Estimation," Economics Working Papers 2007-11, Christian-Albrechts-University of Kiel, Department of Economics.
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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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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