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The Future of Oil: Geology Versus Technology

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The Future of Oil: Geology Versus Technology WP/12/109 The Future of Oil: Geology versus Technology Jaromir Benes, Marcelle Chauvet, Ondra Kamenik, Michael Kumhof, Douglas Laxton, Susanna Mursula and Jack Selody ©International Monetary Fund. Not for Redistribution © 2012 International Monetary Fund WP/12/109 IMF Working Paper Research Department The Future of Oil: Geology versus Technology Prepared by Jaromir Benes, Marcelle Chauvet, Ondra Kamenik, Michael Kumhof, Douglas Laxton, Susanna Mursula and Jack Selody Authorized for distribution by Douglas Laxton May 2012 Abstract This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. We discuss and reconcile two diametrically opposed views concerning the future of world oil production and prices. The geological view expects that physical constraints will dominate the future evolution of oil output and prices. It is supported by the fact that world oil production has plateaued since 2005 despite historically high prices, and that spare capacity has been near historic lows. The technological view of oil expects that higher oil prices must eventually have a decisive effect on oil output, by encouraging technological solutions. It is supported by the fact that high prices have, since 2003, led to upward revisions in production forecasts based on a purely geological view. We 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. The error bands are wide, and reflect sharply differing judgments on ultimately recoverable reserves, and on future price elasticities of oil demand and supply. JEL Classification Numbers: C11, C53, Q31, Q32 Keywords: Oil prices, exhaustible resources; fossil fuels; oil depletion; Hubbert’s Peak; Bayesian econometrics. Author’s E-Mail Address:[email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected] ©International Monetary Fund. Not for Redistribution 2 Contents I.y Introductiony .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 3 II.y Historicaly Forecastsy ofy Worldy Oily Productiony .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 5 IIyI.y They Modely .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 7 A.y Oily Supply.y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 7 B.y Oily Demandy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 9 C.y GDPy Equationsy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 10 1.y Potentialy Levely ofy GDPy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 10 2.y Potentialy Growthy Ratey ofy GDPy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 10 3.y Outputy Gapy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 11 IV.y Analysisy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 11 A.y Impulsey Responsey Functionsy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 11 B.y Interpretationy ofy History.y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 12 C.y Relativey Forecasty Performancey .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 13 D.y Currenty Forecastsy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 14 E.y Oily andy Outputy -y Openy Questionsy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 15 V.y Conclusiony .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 17 Referencesy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 18 Tables 1.y Parametery Estimatesy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 20 2.y Rooty Meany Squarey Errorsy -y Comparisonsy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 20 Figures 1.y EIAy Forecastsy 2001-2010y (EIAy Definitiony ofy Worldy Totaly Oily Supply,y iny Mbd)y 21 2.y Worldy Realy Oily Pricesy andy Sparey Capacity.y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 22 3. Colin Campbell Forecasts 2003-2010 (Campbell Definition of Regular Conven- tionaly Oil,y iny Mbd)y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 23 4. Oil Production Forecasts in the Deffeyes (2005) Model (Q in gigabarrels, q in gigabarrelsy p.a.)y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 24 5.y Impulsey Responsesy (iny percenty levely deviationy fromy control)y .y .y .y .y .y .y .y .y .y .y 25 6.y Historicaly Residualsy (iny percent)y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 26 7.y Contributionsy ofy Differenty Shocksy toy Oily Pricesy (iny realy 2011y USy dollars)y .y .y .y 27 8.y Contributionsy ofy Differenty Shocksy toy Oily Productiony (iny gigabarrelsy p.a.)y .y .y .y 28 9.y Rollingy Forecastsy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 29 10.y Oily Outputy Forecasty withy Errory Bandsy (iny gigabarrelsy p.a.)y .y .y .y .y .y .y .y .y .y .y .y 30 11.y Oily Pricey Forecasty withy Errory Bandsy (iny realy 2011y USy dollars)y .y .y .y .y .y .y .y .y .y 31 12.y GDPy (iny logs)y Forecasty withy Errory Bandsy .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y .y 32 ©International Monetary Fund. Not for Redistribution 3 I. Introduction Future oil prices have been notoriously difficult to predict. In a recent paper, Alquist, Kilian, and Vigfusson (2011) conclude that forecasts based on monthly futures prices, monthly surveys of forecasts, simple econometric models, or other commonly employed forecasting techniques cannot consistently beat a random-walk forecast out of sample. This result is well known within the oil industry. The simple econometric models used by Alquist, Kilian, and Vigfusson (2011) emphasize macroeconomic indicators as predictors of future oil prices. These indicators are highly correlated with fluctuations in aggregate demand, and will therefore mainly capture changes in the price of oil caused by variations in demand. But they are unlikely to be effective in capturing temporary oil supply disruptions. Moreover, since aggregate demand tends to revert to a trend, these variables are not likely to be successful in predicting a long-lasting increase in the price of oil such as the one we have recently observed. However, there is an alternative explanation for the recent persistent price movements that, despite considerable evidence in its support, has received very little attention in the economics literature.
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