Working Paper: CEPR ID: DP9768
Authors: Christiane Baumeister; Pierre Gurin; Lutz Kilian
Abstract: The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models may be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, especially changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred MIDAS model reduces the MSPE by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 82 percent. This MIDAS forecast also is more accurate than a mixed-frequency real-time VAR forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil.
Keywords: forecasts; mixed frequency; oil price; real-time data
JEL Codes: C53; G14; Q43
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Certain predictors (C29) | Forecast accuracy of real price of oil (Q47) |
| Changes in U.S. crude oil inventories (L71) | Real price of oil (Q31) |
| High-frequency financial data (C58) | Forecast accuracy of real price of oil (Q47) |
| High-frequency predictors (C58) | Forecast accuracy of real price of oil (Q47) |
| Use of high-frequency data (C58) | Improved forecasting performance (C53) |