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Structural Change and Forecasting Long-Run Energy Prices

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  • Jean-Thomas Bernard
  • Lynda Khalaf
  • Maral Kichian

Abstract

The authors test the statistical significance of Pindyck’s (1999) suggested class of econometric equations that model the behaviour of long-run real energy prices. The models postulate meanreverting prices with continuous and random changes in their level and trend, and are estimated using Kalman filtering. In such contexts, test statistics are typically non-standard and depend on nuisance parameters. The authors use simulation-based procedures to address this issue; namely, a standard Monte Carlo test and a maximized Monte Carlo test. They find statistically significant instabilities for coal and natural gas prices, but not for crude oil prices. Out-of-sample forecasts are calculated to differentiate between significant models.

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Bibliographic Info

Paper provided by Bank of Canada in its series Working Papers with number 04-5.

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Length: 28 pages
Date of creation: 2004
Date of revision:
Handle: RePEc:bca:bocawp:04-5

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Keywords: Econometric and statistical methods;

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References

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  1. Hamilton, James D., 2003. "What is an oil shock?," Journal of Econometrics, Elsevier, vol. 113(2), pages 363-398, April.
  2. Saphores, J.D. & Khalaf, L. & Pelletier, D., 2000. "On Jumps and ARCH Effects in Natural Resource Prices. An Application to Stumpage Prices from Pacific Northwest National Forests," Papers 00-03, Laval - Recherche en Energie.
  3. Robert A. Amano & Simon van Norden, 1995. "Exchange Rates and Oil Prices," International Finance 9509001, EconWPA.
  4. Robert S. Pindyck, 1999. "The Long-Run Evolutions of Energy Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-27.
  5. Donald W. K. Andrews, 2000. "Inconsistency of the Bootstrap when a Parameter Is on the Boundary of the Parameter Space," Econometrica, Econometric Society, vol. 68(2), pages 399-406, March.
  6. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-30, March.
  7. Marwan Chacra, 2002. "Oil-Price Shocks and Retail Energy Prices in Canada," Working Papers 02-38, Bank of Canada.
  8. Kim, Chang-Jin & Nelson, Charles R, 1989. "The Time-Varying-Parameter Model for Modeling Changing Conditional Variance: The Case of the Lucas Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(4), pages 433-40, October.
  9. DUFOUR, Jean-Marie & FARHAT, Abdeljelil & GARDIOL, Lucien, 1998. "Simulation-Based Finite-Sample Normality Tests in Linear Regressions," Cahiers de recherche 9811, Universite de Montreal, Departement de sciences economiques.
  10. Zellner Arnold, 2002. "My Experiences with Nonlinear Dynamic Models in Economics," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(2), pages 1-18, July.
  11. Jean-Marie Dufour, 1997. "Some Impossibility Theorems in Econometrics with Applications to Structural and Dynamic Models," Econometrica, Econometric Society, vol. 65(6), pages 1365-1388, November.
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Cited by:
  1. Stanislav Radchenko, 2005. "The Long-Run Forecasting of Energy Prices Using the Model of Shifting Trend," Econometrics 0502002, EconWPA.
  2. Khalaf, Lynda & Kichian, Maral, 2005. "Exact tests of the stability of the Phillips curve: the Canadian case," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 445-460, April.
  3. Zhang, Xun & Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method," Energy Economics, Elsevier, vol. 31(5), pages 768-778, September.
  4. Massimiliano Serati & Gianni Amisano, 2008. "Building composite leading indexes in a dynamic factor model framework: a new proposal," LIUC Papers in Economics 212, Cattaneo University (LIUC).

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