| By: |
Bao H. Nguyen;
Bo Zhang |
| Abstract: |
Large Bayesian Vector Autoregressions (BVARs) have been a successful tool in
the forecasting literature and most of this work has focused on macroeconomic
variables. In this paper, we examine the ability of large BVARs to forecast
the real price of crude oil using a large dataset with over 100 variables. We
find consistent results that the large BVARs do not beat the BVARs with small
and medium sizes for short forecast horizons but offer better forecasts at
long horizons. In line with the forecasting macroeconomic literature, we also
find that the forecast ability of the large models further improves upon the
competing standard BVARs once endowed with flexible error structures. |
| Keywords: |
forecasting, non-Gaussian, stochastic volatility, oil prices, big data |
| JEL: |
C11 C32 C52 Q41 Q47 |
| Date: |
2022–10 |
| URL: |
http://d.repec.org/n?u=RePEc:een:camaaa:2022-65&r=for |