Working Paper: CEPR ID: DP10362
Authors: Christiane Baumeister; Lutz Kilian; Thomas K. Lee
Abstract: Although there is much interest in the future retail price of gasoline among consumers, industry analysts, and policymakers, it is widely believed that changes in the price of gasoline are essentially unforecastable given publicly available information. We explore a range of new forecasting approaches for the retail price of gasoline and compare their accuracy with the no-change forecast. Our key finding is that substantial reductions in the mean-squared prediction error (MSPE) of gasoline price forecasts are feasible in real time at horizons up to two years, as are substantial increases in directional accuracy. The most accurate individual model is a VAR(1) model for real retail gasoline and Brent crude oil prices. Even greater reductions in MSPEs are possible by constructing a pooled forecast that assigns equal weight to five of the most successful forecasting models. Pooled forecasts have lower MSPE than the EIA gasoline price forecasts and the gasoline price expectations in the Michigan Survey of Consumers. We also show that as much as 39% of the decline in gas prices between June and December 2014 was predictable.
Keywords: Brent; Expert forecasts; Forecast combination; Oil market; Real-time data; Retail gasoline price; Survey expectations; WTI
JEL Codes: C53; Q43
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Forecasting methods (C53) | MSPE reductions (C30) |
| Bivariate VAR(1) model (C29) | MSPE reductions (C30) |
| Pooled forecast (C53) | MSPE reductions (C30) |
| Forecasting models (C53) | Predictive capacity of models (C52) |
| Crude oil prices (L71) | Gasoline prices (L90) |