Forecasting crude oil prices with DSGE models
Michał Rubaszek ()
No GRU_2019_024, GRU Working Paper Series from City University of Hong Kong, Department of Economics and Finance, Global Research Unit
We run an oil prices forecasting competition among a set of structural models, includ- ing vector autoregressions and dynamic stochastic general equilibrium models. Our results highlights two principles. First, forecasts should exploit the mean reversion of the real oil price over long horizons. Second, models should not replicate the high volatility of oil prices observed in sample. Abiding by these principles, we show that a small scale DSGE model performs much better in real oil price forecasting than the random walk as well as vector autoregressions.
Keywords: Forecasting; oil prices; DSGE models; vector autoregression; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C32 Q35 Q43 Q47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-ene and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:cth:wpaper:gru_2019_024
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