Incorporating Transportation Network Structure in Spatial Econometric Models of Commodity Flows

James LeSage and Wolfgang Polasek

No 188, Economics Series from Institute for Advanced Studies

Abstract: We introduce a regression-based gravity model for commodity flows between 35 regions in Austria. We incorporate information regarding the highway network into the spatial connectivity structure of the spatial autoregressive econometric model. We find that our approach produces improved model fit and higher likelihood values. The model accounts for spatial dependence in the origin-destination flows by introducing a spatial connectivity matrix that allows for three types of spatial dependence in the origins to destinations flows. We modify this origin-destination connectivity structure that was introduced by LeSage and Pace (2005) to include information regarding the presence or absence of a major highway/train corridor that passes through the regions. Empirical estimates indicate that the strongest spatial autoregressive effects arise when both origin and destination regions have neighboring regions located on the highway network. Our approach provides a formal spatial econometric methodology that can easily incorporate network connectivity information in spatial autoregressive models.

Keywords: Commodity flows; Spatial autoregression; Bayesian; Maximum likelihood; Spatial connectivity of origin-destination flows (search for similar items in EconPapers)
JEL-codes: C21 L92 R1 R41 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2006-05
New Economics Papers: this item is included in nep-geo, nep-net and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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https://irihs.ihs.ac.at/id/eprint/1701 First version, 2006 (application/pdf)

Related works:
Journal Article: Incorporating Transportation Network Structure in Spatial Econometric Models of Commodity Flows (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:ihs:ihsesp:188

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