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Bayesian inference for random coefficient dynamic panel data models

Author

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  • Fang Liu
  • Peng Zhang
  • Ibrahim Erkan
  • Dylan S. Small

Abstract

We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit's process to be correlated with the unit-specific coefficients. We impose a stationarity assumption for each unit's process by assuming that the unit-specific autoregressive coefficient is drawn from a logitnormal distribution. Our method is shown to have favorable properties compared to the mean group estimator in a Monte Carlo study. We apply our approach to analyze energy and protein intakes among individuals from the Philippines.

Suggested Citation

  • Fang Liu & Peng Zhang & Ibrahim Erkan & Dylan S. Small, 2017. "Bayesian inference for random coefficient dynamic panel data models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(9), pages 1543-1559, July.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:9:p:1543-1559
    DOI: 10.1080/02664763.2016.1214248
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    References listed on IDEAS

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    1. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    2. Wawro, Gregory, 2002. "Estimating Dynamic Panel Data Models in Political Science," Political Analysis, Cambridge University Press, vol. 10(1), pages 25-48, January.
    3. Alok Bhargava, 2006. "Malnutrition and the Role of Individual Variation with Evidence from India and the Philippines," World Scientific Book Chapters, in: Econometrics, Statistics And Computational Approaches In Food And Health Sciences, chapter 8, pages 113-123, World Scientific Publishing Co. Pte. Ltd..
    4. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    5. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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