Simpler is better: Predicting consumer vehicle purchases in the short run

Abstract

When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010.