When design decisions are informed by consumer choice models, uncertainty in choice model predictions creates uncertainty for the designer. We investigate the variation and accuracy of market share predictions by characterizing fit and forecast accuracy of discrete choice models for the US light duty new vehicle market. Specifically, we estimate multinomial logit models for 9000 utility functions representative of a large literature in vehicle choice modeling using sales data for years 2004–2006. Each model predicts shares for the 2007 and 2010 markets, and we compare several quantitative measures of model fit and predictive accuracy. We find that (1) our accuracy measures are concordant: model specifications that perform well on one measure tend to also perform well on other measures for both fit and prediction. (2) Even the best discrete choice models exhibit substantial prediction error, stemming largely from limited model fit due to unobserved attributes. A naïve “static” model, assuming share for each vehicle design in the forecast year = share in the last available year, outperforms all 9000 attribute-based models when predicting the full market one year forward, but attribute-based models can predict better for four year forward forecasts or new vehicle designs. (3) Share predictions are sensitive to the presence of utility covariates but less sensitive to covariate form (e.g., miles per gallons versus gallons per mile), and nested and mixed logit specifications do not produce significantly more accurate forecasts. This suggests ambiguity in identifying a unique model form best for design. Furthermore, the models with best predictions do not necessarily have expected coefficient signs, and biased coefficients could misguide design efforts even when overall prediction accuracy for existing markets is maximized.
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December 2014
Research-Article
Sensitivity of Vehicle Market Share Predictions to Discrete Choice Model Specification
Jeremy J. Michalek,
Jeremy J. Michalek
Professor
Mechanical Engineering,
Engineering and Public Policy,
e-mail: jmichalek@cmu.edu
Mechanical Engineering,
Engineering and Public Policy,
Carnegie Mellon University
,Pittsburgh, PA 15213
e-mail: jmichalek@cmu.edu
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W. Ross Morrow,
W. Ross Morrow
Assistant Professor
Mechanical Engineering,
e-mail: wrmorrow@iastate.edu
Mechanical Engineering,
Iowa State University
,Ames, IA 50011
e-mail: wrmorrow@iastate.edu
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Yimin Liu
Yimin Liu
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C. Grace Haaf
Jeremy J. Michalek
Professor
Mechanical Engineering,
Engineering and Public Policy,
e-mail: jmichalek@cmu.edu
Mechanical Engineering,
Engineering and Public Policy,
Carnegie Mellon University
,Pittsburgh, PA 15213
e-mail: jmichalek@cmu.edu
W. Ross Morrow
Assistant Professor
Mechanical Engineering,
e-mail: wrmorrow@iastate.edu
Mechanical Engineering,
Iowa State University
,Ames, IA 50011
e-mail: wrmorrow@iastate.edu
Yimin Liu
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received May 28, 2013; final manuscript received June 23, 2014; published online October 20, 2014. Assoc. Editor: Bernard Yannou.
J. Mech. Des. Dec 2014, 136(12): 121402 (9 pages)
Published Online: October 20, 2014
Article history
Received:
May 28, 2013
Revision Received:
June 23, 2014
Connected Content
A companion article has been published:
Erratum: “Sensitivity of Vehicle Market Share Predictions to Discrete Choice Model Specification” [Journal of Mechanical Design, 136(12), 121402]
Citation
Grace Haaf, C., Michalek, J. J., Ross Morrow, W., and Liu, Y. (October 20, 2014). "Sensitivity of Vehicle Market Share Predictions to Discrete Choice Model Specification." ASME. J. Mech. Des. December 2014; 136(12): 121402. https://doi.org/10.1115/1.4028282
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