Journal article
Forecasting light-duty vehicle demand using alternative-specific constants for endogeneity correction versus calibration
Transportation research. Part B: methodological, v 84, pp 182-210
01 Feb 2016
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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. (C) 2015 Elsevier Ltd. All rights reserved.
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Details
- Title
- Forecasting light-duty vehicle demand using alternative-specific constants for endogeneity correction versus calibration
- Creators
- C. Grace Haaf - Carnegie Mellon UniversityW. Ross Morrow - Ford Motor CompanyInes M. L. Azevedo - Carnegie Mellon Univ, Engn & Publ Policy, Pittsburgh, PA 15213 USAElea McDonnell Feit - Drexel UniversityJeremy J. Michalek - Carnegie Mellon Univ, Mech Engn, Pittsburgh, PA 15213 USA
- Publication Details
- Transportation research. Part B: methodological, v 84, pp 182-210
- Publisher
- Elsevier
- Number of pages
- 29
- Grant note
- SES-0949710 / center for Climate and Energy Decision Making National Science Foundation; National Science Foundation (NSF) Carnegie Mellon University 1463492 / Divn Of Social and Economic Sciences; National Science Foundation (NSF); NSF - Directorate for Social, Behavioral & Economic Sciences (SBE) Toyota Motor Corporation Ford Motor Company 1064241 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Marketing
- Web of Science ID
- WOS:000371450300008
- Scopus ID
- 2-s2.0-84953718706
- Other Identifier
- 991019174741404721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Economics
- Engineering, Civil
- Operations Research & Management Science
- Transportation
- Transportation Science & Technology