Border Region Bridge and Air Transport Predictability

Thomas Fullerton () and Somnath Mukhopadhyay

MPRA Paper from University Library of Munich, Germany

Abstract: Border region transportation forecast analysis is fraught with difficulty. In the case of El Paso, Texas and Ciudad Juarez, Chihuahua, Mexico, dual national business cycles and currency market fluctuations further complicate modeling efforts. Incomplete data samples and asymmetric data reporting conventions further confound forecasting exercises. Under these conditions, a natural alternative to structural econometric models to consider is neural network analysis. Neural network forecasts of air transportation and international bridge activity are developed using a multi-layered perceptron approach. Those out-of sample simulations are then compared to previously published forecasts produced with a system of simultaneous econometric equations. Empirical results indicate that the econometric approach is generally more accurate. In several cases, the two sets of forecasts are found to contain complementary information.

Keywords: Regional Transport Demand; Neural Networks; Econometric Forecasting (search for similar items in EconPapers)
JEL-codes: C53 R41 (search for similar items in EconPapers)
Date: 2013-04-11, Revised 2013-07-11
New Economics Papers: this item is included in nep-cmp, nep-for, nep-tre and nep-ure
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Published in Journal of Business & Economics 11.4(2013): pp. 1089-1104

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:59583

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