Fuel Economy Gaps Within & Across Garages: A Bivariate Random Parameters Seemingly Unrelated Regression Approach

TL;DR

This study investigates the variability and interrelationship of fuel economy gaps within garages using advanced statistical models, revealing significant factors influencing real-world fuel efficiency compared to official ratings.

Contribution

It introduces a bivariate random parameters seemingly unrelated regression approach to analyze fuel economy gaps, accounting for within-garage correlation and unobserved heterogeneity.

Findings

Significant variation in fuel economy gaps across garages.

Driving behavior impacts on-road fuel economy relative to official ratings.

Complex relationship between fuel type and fuel economy gaps.

Abstract

The key objective of this study is to investigate the interrelationship between fuel economy gaps and to quantify the differential effects of several factors on fuel economy gaps of vehicles operated by the same garage. By using a unique fuel economy database (fueleconomy.gov), users self-reported fuel economy estimates and government fuel economy ratings are analyzed for more than 7000 garages across the U.S. The empirical analysis, nonetheless, is complicated owing to the presence of important methodological concerns including potential interrelationship between vehicles within the same garage and unobserved heterogeneity. To address these concerns, bivariate seemingly unrelated fixed and random parameter models are presented. With government test cycle ratings tending to over-estimate the actual on-road fuel economy, a significant variation is observed in the fuel economy gaps for the two vehicles across garages. A wide variety of factors such as driving style, fuel economy calculation method, and several vehicle specific characteristics are considered. Drivers who drive for maximum gas mileage or drives with the traffic flow have greater on-road fuel economy relative to the government official ratings. Contrarily, volatile drivers have smaller on-road fuel economy relative to the official ratings. Compared to the previous findings, our analysis suggests that the relationship between fuel type and fuel economy gaps is complex and not unidirectional. Regarding several vehicle and manufacturer related variables, the effects do not just significantly vary in magnitude but also in the direction, underscoring the importance of accounting for within-garage correlation and unobserved heterogeneity for making reliable inferences.