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Semi-parametric spatial autoregressive models in freight generation modeling

Tamás Krisztin ()

Transportation Research Part E: Logistics and Transportation Review, 2018, vol. 114, issue C, 121-143

Abstract: This paper proposes for the purposes of freight generation a spatial autoregressive model framework, combined with non-linear semi-parametric techniques. We demonstrate the capabilities of the model in a series of Monte Carlo studies. Moreover, evidence is provided for non-linearities in freight generation, through an applied analysis of European NUTS-2 regions. We provide evidence for significant spatial dependence and for significant non-linearities related to employment rates in manufacturing and infrastructure capabilities in regions. The non-linear impacts are the most significant in the agricultural freight generation sector.

Keywords: Non-linear spatial autoregressive models; Genetic algorithms; Semi-parametric modeling; Freight generation (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:eee:transe:v:114:y:2018:i:c:p:121-143