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Choice of solutions to the initial-conditions problem in dynamic panel probit models

Author

Listed:
  • Riccardo (Jack) Lucchetti

    (Dipartimento di Scienze Economiche e Sociali (DiSES), Università Politecnica delle Marche)

  • Claudia Pigini

    (Dipartimento di Scienze Economiche e Sociali (DiSES), Università Politecnica delle Marche)

Abstract
Estimation of random-effects dynamic probit models for panel data entails the so-called "initial conditions problem". We argue that the relative finite-sample performance of the two main competing solutions is driven by the magnitude of the individual unobserved heterogeneity and/or of the state dependence in the data. We investigate our conjecture by means of a comprehensive Monte Carlo experiment and offer useful indications for the practitioner.

Suggested Citation

  • Riccardo (Jack) Lucchetti & Claudia Pigini, 2020. "Choice of solutions to the initial-conditions problem in dynamic panel probit models," Working Papers 2020:27, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2020:27
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    References listed on IDEAS

    as
    1. Rabe-Hesketh, Sophia & Skrondal, Anders, 2013. "Avoiding biased versions of Wooldridge’s simple solution to the initial conditions problem," Economics Letters, Elsevier, vol. 120(2), pages 346-349.
    2. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54, January.
    3. Anders Skrondal & Sophia Rabe-Hesketh, 2014. "Handling initial conditions and endogenous covariates in dynamic/transition models for binary data with unobserved heterogeneity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 211-237, February.
    4. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(3), pages 991-1030.
    5. Alpaslan Akay, 2012. "Finite‐sample comparison of alternative methods for estimating dynamic panel data models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1189-1204, November.
    6. Carro, Jesus M., 2007. "Estimating dynamic panel data discrete choice models with fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 503-528, October.
    7. Wiji Arulampalam & Mark B. Stewart, 2009. "Simplified Implementation of the Heckman Estimator of the Dynamic Probit Model and a Comparison with Alternative Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 659-681, October.
    8. F. Bartolucci & R. Bellio & A. Salvan & N. Sartori, 2016. "Modified Profile Likelihood for Fixed-Effects Panel Data Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1271-1289, August.
    9. Francesco Bartolucci & Valentina Nigro, 2010. "A Dynamic Model for Binary Panel Data With Unobserved Heterogeneity Admitting a √n-Consistent Conditional Estimator," Econometrica, Econometric Society, vol. 78(2), pages 719-733, March.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Panel data; dynamic probit; initial conditions;
    All these keywords.

    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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