Forecasting crude oil prices with DSGE models

Michał Rubaszek

International Journal of Forecasting, 2021, vol. 37, issue 2, 531-546

Abstract: In this study, we conducted an oil prices forecasting competition among a set of structural models, including vector autoregression and dynamic stochastic general equilibrium (DSGE) models. Our results highlight two principles. First, forecasts should exploit the fact that real oil prices are mean reverting over long horizons. Second, models should not replicate the high volatility of the oil prices observed in samples. By following these principles, we show that an oil sector DSGE model performs much better at real oil price forecasting than random walk or vector autoregression.

Keywords: Forecasting; Oil prices; DSGE models; Vector autoregression; Bayesian inference (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (8)

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Working Paper: Forecasting crude oil prices with DSGE models (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:2:p:531-546

DOI: 10.1016/j.ijforecast.2020.07.004

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