Estimating flow data models of international trade: dual gravity and spatial interactions

Fei Jin, Lung-fei Lee and Jihai Yu

Econometric Reviews, 2023, vol. 42, issue 2, 157-194

Abstract: This article investigates asymptotic properties of quasi-maximum likelihood (QML) estimates for flow data on the dual gravity model in international trade with spatial interactions (dependence). The dual gravity model has a well-established economic foundation, and it takes the form of a spatial autoregressive (SAR) model. The dual gravity model originates from Behrens et al., but the spatial weights matrix motivated by their economic theory has a feature that violates existing regularity conditions for asymptotic econometrics analysis. By overcoming the limitations of existing asymptotic theory, we show that QML estimates are consistent and asymptotically normal. The simulation results show the satisfactory finite sample performance of the estimates. We illustrate the usefulness of the model by investigating the McCallum “border puzzle” in the gravity literature.

Date: 2023
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DOI: 10.1080/07474938.2023.2178087

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