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A key purpose of the U.S. government fuel economy ratings is to provideprecise and unbiased fuel economy estimates to assist consumers in theirvehicle purchase decisions. For the official fuel economy ratings to be useful,the numbers must be relatively reliable. This study focuses on quantifying thevariations of on-road fuel economy relative to official government ratings(fuel economy gap) and seeks proper characterizations for the degree ofstochastic dependence between the fuel economy gaps of pairs of vehicles.Byusing unique data reported by customers of the U.S. government websitewww.fueleconomy.gov, the study presents an innovative copula-basedjoint-modeling and forecasting framework for exploring the complex stochasticdependencies (both nonlinear and non-normal) between the fuel economy gaps ofvehicles reported by the same person. While the EPA label estimates are similarto the average numbers reported by website customers, significant, non-linearvariation exists in the fuel economy gaps for the two vehicles across thesample. In particular, a positive dependence, characterized by Student-tcopula, is observed between the fuel economy gaps of the two vehicles withsignificant dependencies in the tails of the bivariate distribution; a pair inwhich one vehicle achieves better (worse) fuel economy is likely to contain asecond vehicle getting better (worse) fuel economy as well. However, theresults also suggest that the strength of overall association is weak (KendallTau = 0.28). This implies a lack of compelling agreement between fuel economygaps which could weaken consumers confidence in making relative comparisonsamong vehicles.

Analyzing within Garage Fuel Economy Gaps to Support Vehicle Purchasing Decisions - A Copula-Based Modeling & Forecasting Approach