Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach

Abstract

Given that oil price fluctuations have been of great interest for macroeconomics in the recent years due to its' important implications for future inflation, economic growth, and policy making, the aim of this paper is to estimate and forecast West Texas Intermediate (WTI) crude oil prices using a large monthly dataset, that covers the period from March 1983 to December 2011. To achieve this aim, forecasting with factor models offers a usual approach that utilizes large datasets, however; a forecasting model which simply includes all factors in state space equation and does not allow for time varying may be not suitable with a highly volatile market such as oil market. To overcome these limitations, an approach that accounts both for parameter and model uncertainty is employed. In particular, this study uses the Dynamic Model Averaging (DMA) approach suggested by Koop and Korobilis (2012). The key element of the DMA approach is that it allows both for model and parameter to vary at each point of time. By doing so, the DMA is robust to structural breaks. Empirical findings show that DMA approach outperforms any other alternative model used in the forecasting exercise. Results also show that there is model but not parameter variation in this oil price forecasting exercise. Finally, the findings suggest that the DMA approach provides a better proxy of expected spot prices than future prices.