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Dynamic probit models for panel data: A comparison of three methods of estimation

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  • Alfonso Miranda

    () (Department of Economics, Keele University)

Abstract

Three different methods have been suggested in the econometrics literature to deal with the initial conditions problem in dynamic Probit models for panel data. Heckman (1981) suggest to approximate the reduced form marginal probability of the initial state with a Probit model and allow free correlation between unobserved individual heterogeneity entering the initial conditions and the main dynamic equations. Alternatively, Wooldridge (2002) suggest to write a dynamic model conditional on the first observation and to specify a distribution for the unobserved individual heterogeneity term conditional on the initial state and any other exogenous explanatory variables. Finally, Orme (1996) introduces a two-step bias corrected procedure that is locally valid when the correlation between unobserved individual heterogeneity determining the initial state and the dynamic Probit equations approximates to zero. Orme suggest that this two-step procedure can perform well even when such correlation is strong. I present some results from a Monte Carlo simulation study comparing the performance of all these three methods using small and medium sample sizes and low and high correlation among unobservables.

Suggested Citation

  • Alfonso Miranda, 2007. "Dynamic probit models for panel data: A comparison of three methods of estimation," United Kingdom Stata Users' Group Meetings 2007 11, Stata Users Group.
  • Handle: RePEc:boc:usug07:11
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      • 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.
    3. Giulia Bettin & Riccardo Lucchetti, 2016. "Steady streams and sudden bursts: persistence patterns in remittance decisions," Journal of Population Economics, Springer;European Society for Population Economics, vol. 29(1), pages 263-292, January.
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    5. Tubeuf & Florence Jusot & Damien Bricard, 2011. "Mediating role of education and lifestyles in the relationship betwee early-life conditions and health: evidence from the 1958 British cohort," Working Papers 1106, Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds.
    6. Bruce Hollingsworth & Anthony Scott & Sandy Tubeuf & Florence Jusot & Damien Bricard, 2012. "Mediating Role Of Education And Lifestyles In The Relationship Between Early‐Life Conditions And Health: Evidence From The 1958 British Cohort," Health Economics, John Wiley & Sons, Ltd., vol. 21, pages 129-150, June.
    7. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    8. Sousounis, Panos & Bladen-Hovell, Robin, 2008. "Persistence in work-related training: evidence from the BHPS, 1991-1998," MPRA Paper 9424, University Library of Munich, Germany.
    9. Imisiker, Serkan & Tas, Bedri Kamil Onur, 2013. "Which firms are more prone to stock market manipulation?," Emerging Markets Review, Elsevier, vol. 16(C), pages 119-130.

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