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Convenient estimators for the panel probit model: Further results

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  • William Greene

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Abstract

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

Suggested Citation

  • William Greene, 2004. "Convenient estimators for the panel probit model: Further results," Empirical Economics, Springer, vol. 29(1), pages 21-47, January.
  • Handle: RePEc:spr:empeco:v:29:y:2004:i:1:p:21-47
    DOI: 10.1007/s00181-003-0187-z
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    Cited by:

    1. Wang, Hui & Riedinger, Jeffrey & Jin, Songqing, 2015. "Land documents, tenure security and land rental development: Panel evidence from China," China Economic Review, Elsevier, vol. 36(C), pages 220-235.
    2. Udo Schneider & Volker Ulrich, 2007. "Health Relevant Behavior and its Impact on the Physician-Patient Relationship," Working Papers 023, Bavarian Graduate Program in Economics (BGPE).
    3. Wladimir Raymond & Pierre Mohnen & Franz Palm & Sybrand Schim van der Loeff, 2007. "The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models," CESifo Working Paper Series 1992, CESifo Group Munich.
    4. Zhang, Xiao & Boscardin, W. John & Belin, Thomas R. & Wan, Xiaohai & He, Yulei & Zhang, Kui, 2015. "A Bayesian method for analyzing combinations of continuous, ordinal, and nominal categorical data with missing values," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 43-58.
    5. Sirchenko Andrei, 2012. "A model for ordinal responses with an application to policy interest rate," EERC Working Paper Series 12/13e, EERC Research Network, Russia and CIS.
    6. Díaz Serrano, Luis & Stoyanova, Alexandrina Petrova, 2009. "Mobility and Housing Satisfaction: An Empirical Analysis for Twelve EU Countries," Working Papers 2072/42895, Universitat Rovira i Virgili, Department of Economics.
    7. Federica Liberini, 2014. "Corporate Taxes and the Growth of the Firm," KOF Working papers 14-354, KOF Swiss Economic Institute, ETH Zurich.
    8. Kim, Soo-Il & Gopinath, Munisamy & Kim, Hanho, 2009. "High productivity before or after exports? An empirical analysis of Korean manufacturing firms," Journal of Asian Economics, Elsevier, vol. 20(4), pages 410-418, September.
    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.
    10. Olaf Hübler, 2006. "Multilevel and nonlinear panel data models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 121-136, March.
    11. Hyytinen, Ari & Pajarinen, Mika, 2004. "Opacity of Young Firms: Faith or Fact?," Discussion Papers 923, The Research Institute of the Finnish Economy.
    12. Aßmann, Christian & Boysen-Hogrefe, Jens, 2011. "A Bayesian approach to model-based clustering for binary panel probit models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 261-279, January.
    13. Udo Schneider & Volker Ulrich, 2008. "The physician-patient relationship revisited: the patient’s view," International Journal of Health Economics and Management, Springer, vol. 8(4), pages 279-300, December.
    14. John Mullahy, 2017. "Marginal effects in multivariate probit models," Empirical Economics, Springer, vol. 52(2), pages 447-461, March.
    15. Hübler, Olaf, 2005. "Panel Data Econometrics: Modelling and Estimation," Hannover Economic Papers (HEP) dp-319, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    16. Martin Burda & Roman Liesenfeld & Jean-Francois Richard, 2008. "Bayesian Analysis of a Probit Panel Data Model with Unobserved Individual Heterogeneity and Autocorrelated Errors," Working Papers tecipa-321, University of Toronto, Department of Economics.
    17. Belderbos, Rene & Carree, Martin & Diederen, Bert & Lokshin, Boris & Veugelers, Reinhilde, 2004. "Heterogeneity in R&D cooperation strategies," International Journal of Industrial Organization, Elsevier, vol. 22(8-9), pages 1237-1263, November.
    18. Roman Liesenfeld & Guilherme Valle Moura & Jean-François Richard, 2010. "Determinants and Dynamics of Current Account Reversals: An Empirical Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 486-517, August.
    19. Liberini, Federica, 2014. "Corporate Taxes and the Growth of the Firm," The Warwick Economics Research Paper Series (TWERPS) 1042, University of Warwick, Department of Economics.
    20. Jürgen Bierbaumer-Polly & Werner Hölzl, 2016. "Business Cycle Dynamics and Firm Heterogeneity. Evidence for Austria Using Survey Data," WIFO Working Papers 504, WIFO.
    21. Silja Göhlmann & Christoph M. Schmidt & Harald Tauchmann, 2010. "Smoking initiation in Germany: the role of intergenerational transmission," Health Economics, John Wiley & Sons, Ltd., vol. 19(2), pages 227-242.
    22. Zhiyang Jia, 2005. "Spousal Influence on Early Retirement Behavior," Discussion Papers 406, Statistics Norway, Research Department.

    More about this item

    Keywords

    Panel probit model; multivariate probit; GMM; simulated likelihood; latent class; marginal effects; C14; C23; C25;

    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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