Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models

Yudong Wang, Li Liu and Chongfeng Wu

Energy Economics, 2017, vol. 66, issue C, 337-348

Abstract: In this paper, we forecast real prices of crude oil using real-time forecast combinations over time-varying parameter (TVP) models with single predictor. We reveal the significant predictability at all horizons up to 24months. The mean squared predictive error reduction over the benchmark of no-change forecast is as high as 17% and the directional accuracy as high as 0.645. A combination with TVP models is found to generate more accurate forecasts than the same combination with constant coefficient models because the forecast errors of individual TVP models are correlated at a lower degree. We also evaluate the forecasting performance in the framework of density forecasting. Our results indicate that the benchmark model can be significantly outperformed by forecast combination at the horizons longer than 3months.

Keywords: Real oil prices; Time-varying parameter; Forecasting combination; Predictive regression; Density forecasting (search for similar items in EconPapers)
JEL-codes: C32 C53 E31 E32 Q41 Q43 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (58)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:66:y:2017:i:c:p:337-348

DOI: 10.1016/j.eneco.2017.07.007

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