A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade
Quaderni - Working Paper DSE N° 1081
21 Pages Posted: 18 Oct 2016
There are 2 versions of this paper
A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade
A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade
Date Written: October 10, 2016
Abstract
Nonlinear estimation of the gravity model with Poisson/negative binomial methods has become popular to model international trade flows, because it permits a better accounting for zero flows and extreme values in the distribution tail. Nevertheless, as trade flows are not independent from each other due to spatial autocorrelation, these methods may lead to biased parameter estimates. To overcome this problem, eigenvector spatial filtering variants of the Poisson/negative binomial specification have been proposed in the literature of gravity modelling of trade. However, no specific treatment has been developed for cases in which many zero flows are present. This paper contributes to the literature in two ways. First, by employing a stepwise selection criterion for spatial filters that 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 processes of zero-inflated methods. 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.
Keywords: bilateral trade, unconstrained gravity model, eigenvector spatial filtering, zero flows, backward stepwise, zero-inflation
JEL Classification: C14, C21, F10
Suggested Citation: Suggested Citation