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
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)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:transe:v:114:y:2018:i:c:p:121-143
Ordering information: This journal article can be ordered from
http://www.elsevier. ... 600244/bibliographic
Access Statistics for this article
Transportation Research Part E: Logistics and Transportation Review is currently edited by W. Talley
More articles in Transportation Research Part E: Logistics and Transportation Review from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().