Network dependence in multi-indexed data on international trade flows
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ORIGINAL PAPER
Network dependence in multi-indexed data on international trade flows Manfred M. Fischer1
•
James P. LeSage2
Received: 14 April 2020 / Accepted: 22 September 2020 Ó The Author(s) 2020
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
JEL Classification C18 C33 C51 & Manfred M. Fischer [email protected] http://sites.google.com/view/professormanfredmfischer James P. LeSage [email protected] 1
Vienna University of Economics and Business, Vienna, Austria
2
Texas State University, San Marcos, TX, USA
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M. M. Fischer, J. P. LeSage
1 Introduction In recent years fixed effects model specifications have enjoyed great popularity in the panel gravity literature as a way to take into account the specific threedimensional nature of international trade flow data sets.1 These models, differing in the specification of the fixed effects, focus on unobserved heterogeneity, but ignore any potential cross-sectional dependence due to network interactions. Faced with this problem, 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 (MCMC) estimation methods that produce a Monte Carlo integration estimate of the log-marginal likelihood that can be used for model comparison. Empirical international trade modeling has focused on multilateral
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