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Bayesian estimation of dynamic panel data gravity model

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  • Moonhee Cho
  • Xiaoyong Zheng

Abstract

In this paper, we develop Bayesian estimation method for inference of dynamic panel data gravity model. Our method deals with the many zeros problem and at the same time, allows for lagged dependent variables and multiple sets of unobserved effects. We apply our Bayesian estimation algorithm to reexamine the contemporaneous effect of GATT/WTO membership on trade. We find that our dynamic gravity model fits the data better than the same model without the lagged dependent variables that is often used in the literature and trade flow in the previous period has a large and positive effect on trade flow in the current period. We also find that the GATT/WTO membership does not appear to have a contemporaneous effect on trade flow. This result is consistent with the findings ofsome studies in the literature, but not with those of others. These results show the importance of including lagged dependent variables and multiple sets of unobserved effects in gravity model estimation.

Suggested Citation

  • Moonhee Cho & Xiaoyong Zheng, 2021. "Bayesian estimation of dynamic panel data gravity model," Econometric Reviews, Taylor & Francis Journals, vol. 40(7), pages 607-634, August.
    • Handle: RePEc:taf:emetrv:v:40:y:2021:i:7:p:607-634
    DOI: 10.1080/07474938.2021.1889203
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