Forecasting crude oil prices with DSGE models

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 are mean reverting over long horizons. Second, models not replicate high volatility observed in samples. By following these principles, show sector DSGE model performs much better at price than random walk or autoregression.