Bilateral trade flows traditionally have been analysed by means of the spatial interaction gravity model. Still, (auto)correlation of trade flows has only recently received attention in the literature. This paper takes up this thread of emerging literature, and shows that spatial filtering (SF) techniques can take into account the autocorrelation in trade flows. Furthermore, we show that the use of origin and destination specific spatial filters goes a long way in correcting for omitted variable bias in an otherwise standard empirical gravity equation. For a cross-section of bilateral trade flows, we compare an SF approach to two benchmark specifications that are consistent with theoretically derived gravity. The results are relevant for a number of reasons. First, we correct for autocorrelation in the residuals. Second, we suggest that the empirical gravity equation can still be considered in applied work, despite the theoretical arguments for its misspecification due to omitted multilateral resistance terms. Third, if we include SF variables, we can still resort to any desired estimator, such as OLS, Poisson or negative binomial regression. Finally, interpreting endogeneity bias as autocorrelation in regressor variables and residuals allows for a more general specification of the gravity equation than the relatively restricted theoretical gravity equation. In particular, we can include additional country-specific push and pull variables, besides GDP (e.g., land area, landlockedness, and per capita GDP). A final analysis provides autocorrelation diagnostics according to different candidate indicators.

The Space of Gravity: Spatial Filtering Estimation of a Gravity Model for Bilateral Trade

PATUELLI, ROBERTO;
2016

Abstract

Bilateral trade flows traditionally have been analysed by means of the spatial interaction gravity model. Still, (auto)correlation of trade flows has only recently received attention in the literature. This paper takes up this thread of emerging literature, and shows that spatial filtering (SF) techniques can take into account the autocorrelation in trade flows. Furthermore, we show that the use of origin and destination specific spatial filters goes a long way in correcting for omitted variable bias in an otherwise standard empirical gravity equation. For a cross-section of bilateral trade flows, we compare an SF approach to two benchmark specifications that are consistent with theoretically derived gravity. The results are relevant for a number of reasons. First, we correct for autocorrelation in the residuals. Second, we suggest that the empirical gravity equation can still be considered in applied work, despite the theoretical arguments for its misspecification due to omitted multilateral resistance terms. Third, if we include SF variables, we can still resort to any desired estimator, such as OLS, Poisson or negative binomial regression. Finally, interpreting endogeneity bias as autocorrelation in regressor variables and residuals allows for a more general specification of the gravity equation than the relatively restricted theoretical gravity equation. In particular, we can include additional country-specific push and pull variables, besides GDP (e.g., land area, landlockedness, and per capita GDP). A final analysis provides autocorrelation diagnostics according to different candidate indicators.
2016
Spatial Econometric Interaction Modelling
145
169
R. Patuelli; G.-J.Linders; R. Metulini; D.A. Griffith
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/474966
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