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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

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Bibliographic Info

Article provided by Springer in its journal Empirical Economics.

Volume (Year): 29 (2004)
Issue (Month): 1 (January)
Pages: 21-47

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Handle: RePEc:spr:empeco:v:29:y:2004:i:1:p:21-47

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Related research

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

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Cited by:
  1. 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.
  2. 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," CIRANO Working Papers 2007s-06, CIRANO.
  3. 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.
  4. Olaf Hübler, 2006. "Multilevel and nonlinear panel data models," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 121-136, March.
  5. Hyytinen, Ari & Pajarinen, Mika, 2004. "Opacity of Young Firms: Faith or Fact?," Discussion Papers 923, The Research Institute of the Finnish Economy.
  6. Udo Schneider & Volker Ulrich, 2005. "The Physician-Patient Relationship Revisited - the Patient's View," HEW 0505001, EconWPA.
  7. 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.
  8. 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.
  9. Federica Liberini, 2014. "Corporate Taxes and the Growth of the Firm," KOF Working papers 14-354, KOF Swiss Economic Institute, ETH Zurich.
  10. Hübler, Olaf, 2005. "Panel Data Econometrics: Modelling and Estimation," Hannover Economic Papers (HEP) dp-319, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  11. 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.
  12. 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.
  13. 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.
  14. Zhiyang Jia, 2005. "Spousal Influence on Early Retirement Behavior," Discussion Papers 406, Research Department of Statistics Norway.

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