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Cristiana Belu Mănescu
- 18 September 2014
- WORKING PAPER SERIES - No. 1735Details
- This paper demonstrates how the real-time forecasting accuracy of different Brent oil price forecast models changes over time. We find considerable instability in the performance of all models evaluated and argue that relying on average forecasting statistics might hide important information on a model`s forecasting properties. To address this instability, we propose a forecast combination approach to predict quarterly real Brent oil prices. A four-model combination (consisting of futures, risk-adjusted futures, a Bayesian VAR and a DGSE model of the oil market) predicts Brent oil prices more accurately than the futures and the random walk up to 11 quarters ahead, on average, and generates a forecast whose performance is remarkably robust over time. In addition, the model combination reduces the forecast bias and predicts the direction of the oil price changes more accurately than both benchmarks.
- JEL Code
- Q43 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Energy and the Macroeconomy
C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
- 25 September 2015
- WORKING PAPER SERIES - No. 1855Details
- The aim of this paper is to analyze the impact of the so-called
- JEL Code
- Q41 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Demand and Supply, Prices
Q47 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Energy Forecasting
E17 : Macroeconomics and Monetary Economics→General Aggregative Models→Forecasting and Simulation: Models and Applications