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Research Papers: Design Automation

Sensitivity of Vehicle Market Share Predictions to Discrete Choice Model Specification

[+] Author and Article Information
C. Grace Haaf

Mechanical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: grace.haaf@gmail.com

Jeremy J. Michalek

Professor
Mechanical Engineering,
Engineering and Public Policy,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: jmichalek@cmu.edu

W. Ross Morrow

Assistant Professor
Mechanical Engineering,
Iowa State University,
Ames, IA 50011
e-mail: wrmorrow@iastate.edu

Yimin Liu

Technical Expert
Ford Motor Company,
Dearborn, MI 48121
e-mail: Yliu59@ford.com

An electronic companion to this paper containing the appendices referenced herein can be found at http://repository.cmu.edu/meche/70/

We use the term “vehicle design” to refer to vehicle make-model.

This is distinct from the “class dummies only logit” which includes data for the entire market but uses only dummies representing each class as covariates.

A likelihood ratio test of the best logit and mixed logit models calculated on 2007 data suggests that there is sufficient evidence to reject the null hypothesis that the mixed logit model predicts significantly better at the α = 0.1 level.

We reject the null hypothesis that the coefficient is equal to zero at the α = 0.01 level for a two-sided t-test.

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 136(12), 121402 (Oct 20, 2014) (9 pages) Paper No: MD-13-1232; doi: 10.1115/1.4028282 History: Received May 28, 2013; Revised June 23, 2014

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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Figures

Grahic Jump Location
Fig. 1

CDF of error tolerance for the best logit model specifications as measured by likelihood/KL and AIC/BIC measures on 2004–2006 sales estimation data and 2007 sales prediction data compared to alternative models (a) full market and (b) new vehicle designs only

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