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Bayesian model averaging for dynamic panels with an application to a trade gravity model

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  • Huigang Chen
  • Alin Mirestean
  • Charalambos G. Tsangarides

Abstract

We extend the Bayesian Model Averaging (BMA) framework to dynamic panel data models with endogenous regressors using a Limited Information Bayesian Model Averaging (LIBMA) methodology. Monte Carlo simulations confirm the asymptotic performance of our methodology both in BMA and selection, with high posterior inclusion probabilities for all relevant regressors, and parameter estimates very close to their true values. In addition, we illustrate the use of LIBMA by estimating a dynamic gravity model for bilateral trade. Once model uncertainty, dynamics, and endogeneity are accounted for, we find several factors that are robustly correlated with bilateral trade. We also find that applying methodologies that do not account for either dynamics or endogeneity (or both) results in different sets of robust determinants.

Suggested Citation

  • Huigang Chen & Alin Mirestean & Charalambos G. Tsangarides, 2018. "Bayesian model averaging for dynamic panels with an application to a trade gravity model," Econometric Reviews, Taylor & Francis Journals, vol. 37(7), pages 777-805, August.
    • Handle: RePEc:taf:emetrv:v:37:y:2018:i:7:p:777-805
    DOI: 10.1080/07474938.2016.1167857
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    Cited by:

    1. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    2. Abeliansky, Ana Lucia & Barbero, Javier & Rodriguez-Crespo, Ernesto, 2021. "ICTs quality and quantity and the margins of trade," Telecommunications Policy, Elsevier, vol. 45(1).

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