A spatial-filtering zero-inflated approach to the estimation of the gravity model of trade

Abstract

Nonlinear estimation of the gravity model with Poisson/negative binomial methods has become popular to model international trade flows, as it permits a better accounting for large numbers of zero flows. Nevertheless, as trade flows are not independent of each other due to spatial autocorrelation, those methods lead to biased parameter estimates. To overcome this problem, eigenvector spatial filtering variants of the Poisson/Negative binomial specification has been proposed in the literature of gravity modelling of trade. This paper contributes to the literature in two ways. First, by employing a stepwise selection criterion for spatial filters which is based on robust (sandwich) p-values and does not require likelihood-based indicators. In this respect, we develop an ad hoc backward stepwise function in R. Second, using this function, we select a reduced set of spatial filters that properly accounts for importer-side and exporter-side specific spatial effects, both at the count and the logit process. Applying this estimation strategy to a cross-section of bilateral trade flows between a set of worldwide countries for the year 2000, we find that our specification outperforms the benchmark models, in terms of model fitting, both considering the AIC and in predicting zero (and small) flows.