Forecasting light-duty vehicle demand using alternative-specific constants for endogeneity correction versus calibration

• We investigate parameter recovery and forecast accuracy of two methods of incorporating alternative-specific constants (ASCs) in automotive choice models.
• We examine a synthetic case that mimics the structure of typical automotive data as well as a market case using US automotive sales data.
• The maximum likelihood method with post-hoc ASC calibration (MLE-C) can produce biased coefficients but yields better forecasts in most of the cases examined.
• The generalized method of moments with valid instrumental variables (GMM-IV) can mitigate endogeneity bias yet often predicts worse in both synthetic and market case studies.
• We find evidence that the instruments most frequently used in the automotive demand literature may be invalid.

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.