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Network dependence in multi-indexed data on international trade flows

Manfred Fischer and James LeSage

Journal of Spatial Econometrics, 2020, vol. 1, issue 1, 1-26

Abstract: Abstract Faced with the problem that conventional multidimensional fixed effects models only focus on unobserved heterogeneity, but ignore any potential cross-sectional dependence due to network interactions, we introduce a model of trade flows between countries over time that allows for network dependence in flows, based on sociocultural connectivity structures. We show that conventional multidimensional fixed effects model specifications exhibit cross-sectional dependence between countries that should be modeled to avoid simultaneity bias. Given that the source of network interaction is unknown, we propose a panel gravity model that examines multiple network interaction structures, using Bayesian model probabilities to determine those most consistent with the sample data. This is accomplished with the use of computationally efficient Markov Chain Monte Carlo estimation methods that produce a Monte Carlo integration estimate of the log-marginal likelihood that can be used for model comparison. Application of the model to a panel of trade flows points to network spillover effects, suggesting the presence of network dependence and biased estimates from conventional trade flow specifications. The most important sources of network dependence were found to be membership in trade organizations, historical colonial ties, common currency, and spatial proximity of countries.

Keywords: Origin-destination panel data flows; Cross-sectional dependence; MCMC estimation; Log-marginal likelihood; Gravity models of trade; Sociocultural distance (search for similar items in EconPapers)
JEL-codes: C18 C33 C51 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s43071-020-00005-w

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Journal of Spatial Econometrics is currently edited by Giuseppe Arbia, Lung Fei Lee and James LeSage

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