Forecasting light-duty vehicle demand using alternative-specific constants for endogeneity correction versus calibration |
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| Institution: | 1. Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA;2. Ford Motor Company, Ford Research and Innovation Center, 3200 Hillview, #200, Palo Alto, CA 94304, USA;3. Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA;4. Marketing, Drexel University, Philadelphia, PA 19104, USA;2. Disaster Mitigation Research Center, Nagoya University, Nagoya, Japan;3. Graduate School of Education, Okayama University, Okayama, Japan;1. Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Station 18, Lausanne 1015, Switzerland;2. Haute École de Gestion de Genève, University of Applied Sciences Western Switzerland (HES-SO), Campus Battelle, Bâtiment F, Route de Drize 7, Carouge 1227, Switzerland;1. Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, HK;2. Manufacturing and Industrial Engineering Cluster, School of MAE, Nanyang Technological University (NTU), Singapre;2. Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan |
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| Abstract: | We investigate parameter recovery and forecast accuracy implications of incorporating alternative-specific constants (ASCs) in the utility functions of vehicle choice models. We compare two methods of incorporating ASCs: (1) a maximum likelihood estimator that computes ASCs post-hoc as calibration constants (MLE-C) and (2) a generalized method of moments estimator that uses instrumental variables (GMM-IV) to correct for price endogeneity. In a synthetic study we observe significant coefficient bias with MLE-C when the price-ASC correlation (endogeneity) is large. GMM-IV successfully mitigates this bias given valid instruments but exacerbates the bias given invalid instruments. Despite greater coefficient bias, MLE-C yields better forecasts than GMM-IV with valid instruments in most of the cases examined, including most cases where the price-ASC correlation present in the estimation data is absent in the prediction data. In a market study of U.S. midsize sedan sales from 2002 – 2006 the GMM-IV model predicts the 1-year-forward market better, but the MLE-C model predicts the 5-year-forward market better. Including an ASC in predictions by any of the methods proposed improves share forecasts, and assuming that the ASC of each new vehicle matches that of its closest competitor vehicle yields the best long term forecasts. We find evidence that the instruments most frequently used in the automotive demand literature may be invalid. |
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