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State dependence in work-related training participation among British employees: A comparison of different random effects probit estimators

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  • Panos, Sousounis

Abstract

This paper compares three different estimation approaches for the random effects dynamic panel data model, under the probit assumption on the distribution of the errors. These three approaches are attributed to Heckman (1981), Wooldridge (2005) and Orme (2001). The results are then compared with those obtained from generalised method of moments (GMM) estimators of a dynamic linear probability model, namely the Arellano and Bond (1991) and Blundell and Bond (1998) estimators. A model of work-related training participation for British employees is estimated using individual level data covering the period 1991-1997 from the British Household Panel Survey. This evaluation adds to the existing body of empirical evidence on the performance of these estimators using real data, which supplements the conclusions from simulation studies. The results suggest that for the dynamic random effects probit model the performance of no one estimator is superior to the others. GMM estimation of a dynamic LPM of training participation suggests that the random effects estimators are not sensitive to the distributional assumptions of the unobserved effect.

Suggested Citation

  • Panos, Sousounis, 2008. "State dependence in work-related training participation among British employees: A comparison of different random effects probit estimators," MPRA Paper 14261, University Library of Munich, Germany, revised Mar 2009.
  • Handle: RePEc:pra:mprapa:14261
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    File URL: https://mpra.ub.uni-muenchen.de/14261/1/MPRA_paper_14261.pdf
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. 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.
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    4. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    5. Arulampalam, W., 1998. "A Note on Estimated Coefficients in Random Effects Probit Models," The Warwick Economics Research Paper Series (TWERPS) 520, University of Warwick, Department of Economics.
    6. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    7. Wooldridge, Jeffrey M., 1995. "Selection corrections for panel data models under conditional mean independence assumptions," Journal of Econometrics, Elsevier, vol. 68(1), pages 115-132, July.
    8. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
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    13. Arellano, Manuel & Honore, Bo, 2001. "Panel data models: some recent developments," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 53, pages 3229-3296 Elsevier.
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    Cited by:

    1. Maciej Bukowski & Sonia Buchholtz & Piotr Lewandowski & Pawel Chrostek & Agnieszka Kaminska & Maciej Lis & Monika Potoczna & Michal Myck & Michal Kundera & Monika Oczkowska, 2013. "Employment in Poland 2011. Poverty and Jobs," Books and Reports published by IBS, Instytut Badan Strukturalnych, number zwp2011 edited by Maciej Bukowski & Iga Magda, january.
      • Magda, Iga & Bukowski, Maciej & Buchholz, Sonia & Lewandowski, Piotr & Chrostek, Paweł & Kamińska, Agnieszka & Lis, Maciej & Potoczna, Monika & Myck, Michał & Kundera, Michał & Oczkowska, Monika, 2013. "Employment in Poland 2011 - Poverty and jobs," MPRA Paper 50185, University Library of Munich, Germany.

    More about this item

    Keywords

    state dependence; training; dynamic panel data models;

    JEL classification:

    • 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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