Energy Economics

"The Future of Oil: Geology versus Technology",  with Benes, J., Kamenik, O., Kumhof, M., Laxton, D., Mursula, S., Selody, J., International Journal of Forecasting, vol. 31:1, 207-221, 2014. Recipient of the 2015-2016 Outstanding Paper Award  from the International Journal of Forecasting. (Download or IJF)

Abstract: We discuss and reconcile the geological and economic/technological views concerning the future of world oil production and prices, and present a nonlinear econometric model of the world oil market that encompasses both views. The model performs far better than existing empirical models in forecasting oil prices and oil output out-of-sample. Its point forecast is for a near doubling of the real price of oil over the coming decade, though the error bands are wide, reflecting sharply differing judgments on the ultimately recoverable reserves, and on future price elasticities of oil demand and supply.

"vOILatility: Forecasting Oil Prices under Uncertainty," mimeo, University of California Riverside, 2018. 

Abstract: "Historically, oil prices are subject to sudden jumps as well as smoother changes due to changes in supply and demand. Although linear models may capture some of the dynamics in between the jumps in-sample, they fail to represent and predict nonlinearities underlying the market out-of-sample, real time. Some of the abrupt changes in oil price dynamics were due to OPEC decisions in the 1970s-1980s. Recent developments such as shifts to new technology or cooperation of Russia and OPEC can potential engender new structural breaks in the oil market dynamics, with the possibility of markedly different results in out of sample real time forecasts. Models and methods that take into account instability/breaks might substantially improve forecasts. Nonlinear models reveal additional information compared to frameworks that take into account only average linear effect of one series on another. This paper proposes a model specifically designed to forecast oil prices taking into account potential nonlinearities and nonstationarities. The autoregressive multivariate mixed frequency model has probabilities of structural breaks in the mean and volatility of oil price as a function of several variables including: indicators of potential sudden changes in oil supply/price (news on OPEC, Russia’s oil policy and changes in inventories), indicator of economywide demand and oil consumption in the largest consumers and importers of oil, indicator of recent technology shifts, indicator of changes in risk. Preliminary results indicate that the model provides accurate real-time forecast of oil price remarkably superior to forecasts from alternative linear frameworks."