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Misspecified heteroskedasticity in the panel probit model: A small sample comparison of GMM and SML estimators

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  • Inkmann, Joachim

Abstract

This paper compares generalized method of moments (GMM) and simulated maximum likeli- hood (SML) approaches to the estimation of the panel probit model. Both techniques circum- vent multiple integration of joint density functions without the need to restrict the error term variance-covariance matrix of the latent normal regression model. Particular attention is paid to a three-stage GMM estimator based on nonparametric estimation of the optimal instru- ments for given conditional moment functions. Monte Carlo experiments are carried out which focus on the small sample consequences of misspecification of the error term variance- covariance matrix. The correctly specified experiment reveals the asymptotic efficiency ad- vantages of SML. The GMM estimators outperform SML in the presence of misspecification in terms of multiplicative heteroskedasticity. This holds in particular for the three-stage GMM estimator. Allowing for heteroskedasticity over time increases the robustness with respect to misspecification in terms of ultiplicative heteroskedasticity. An application to the product innovation activities of German manufacturing firms is presented. Classification-JEL: C14, C15, C23, C25

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  • Inkmann, Joachim, 1999. "Misspecified heteroskedasticity in the panel probit model: A small sample comparison of GMM and SML estimators," CoFE Discussion Papers 99/04, University of Konstanz, Center of Finance and Econometrics (CoFE).
    • Handle: RePEc:zbw:cofedp:9904
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    Cited by:

    1. Giorgio Calzolari & Laura Magazzini, 2012. "Autocorrelation and masked heterogeneity in panel data models estimated by maximum likelihood," Empirical Economics, Springer, vol. 43(1), pages 145-152, August.
    2. Inkmann, Joachim, 2001. "Accounting for Nonresponse Heterogeneity in Panel Data," CoFE Discussion Papers 01/03, University of Konstanz, Center of Finance and Econometrics (CoFE).
    3. Hujer, Reinhard & Wellner, Marc, 2000. "Berufliche Weiterbildung und individuelle Arbeitslosigkeitsdauer in West- und Ostdeutschland : eine mikroökonometrische Analyse (Further vocational training and individual duration ofunemployment in w," Mitteilungen aus der Arbeitsmarkt- und Berufsforschung, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 33(3), pages 405-420.
    4. Hujer, Reinhard & Wellner, Marc, 2000. "Berufliche Weiterbildung und individuelle Arbeitslosigkeitsdauer in West- und Ostdeutschland : eine mikroökonometrische Analyse (Further vocational training and individual duration of unemployment in ," Mitteilungen aus der Arbeitsmarkt- und Berufsforschung, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 33(3), pages 405-420.
    5. Joachim Inkmann, 2000. "Finite Sample Properties of One-Step, Two-Step and Bootstrap Empirical Likelihood Approaches to Efficient GMM Estimation," Econometric Society World Congress 2000 Contributed Papers 0332, Econometric Society.
    6. 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.
    7. Inkmann, J., 2005. "Inverse Probability Weighted Generalised Empirical Likelihood Estimators : Firm Size and R&D Revisited," Discussion Paper 2005-131, Tilburg University, Center for Economic Research.
    8. Elias Ilin & Laurence J. Kotlikoff & Melinda Pitts, 2022. "Is Our Fiscal System Discouraging Marriage? A New Look at the Marriage Tax," NBER Working Papers 30159, National Bureau of Economic Research, Inc.
    9. Michael Lechner & Stefan Lollivier & Thierry Magnac, 2005. "Parametric Binary Choice Models," University of St. Gallen Department of Economics working paper series 2005 2005-23, Department of Economics, University of St. Gallen.
    10. Hujer, Reinhard & Wellner, Marc, 2000. "The Effects of Public Sector Sponsored Training on Individual Employment Performance in East Germany," IZA Discussion Papers 141, Institute of Labor Economics (IZA).
    11. González, M. & Minguez, R., 2005. "The Method Of Simulated Maximum Likelihood For The Estimaton Of Dynamic Ordered Probit: An Application To Country-Risk For Non-Developed Countries," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(3), pages 99-133.
    12. Calzolari, Giorgio & Magazzini, Laura & Mealli, Fabrizia, 2001. "Simulation-based estimation of Tobit model with random effects," MPRA Paper 22985, University Library of Munich, Germany, revised 2001.
    13. Ziegler Andreas, 2010. "Z-Tests in Multinomial Probit Models under Simulated Maximum Likelihood Estimation: Some Small Sample Properties," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(5), pages 630-652, October.
    14. Andreas Ziegler, 2007. "Simulated classical tests in multinomial probit models," Statistical Papers, Springer, vol. 48(4), pages 655-681, October.

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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
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation 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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