IDEAS home Printed from https://ideas.repec.org/p/apl/wpaper/05-20.html
   My bibliography  Save this paper

Nonrenewable Resource Prices: Deterministic or Stochastic Trends?

Author

Listed:
  • Junsoo Lee
  • John A. List
  • Mark C. Strazicich

Abstract

In this paper we examine temporal properties of eleven natural resource real price series from 1870-1990. Recent studies by Ahrens and Sharma [1997], Berck and Roberts [1996], and Slade [1988], among others, find that many nonrenewable resource prices have a stochastic trend. We revisit this issue by employing a Lagrangian Multiplier unit root test that allows for two endogenously determined structural breaks with and without a quadratic trend. Contrary to previous research, we find evidence against the unit root hypothesis for all price series. Our findings support characterizing natural resource prices as stationary around deterministic trends with structural breaks. We additionally show that both pre-testing for unit roots with breaks and allowing for breaks in the forecast model can improve forecast accuracy. Overall, the results in this paper are important in both a positive and normative sense; without an appropriate understanding of the dynamics of a time series, empirical verification of theories, forecasting, and proper inference are potentially fruitless.

Suggested Citation

  • Junsoo Lee & John A. List & Mark C. Strazicich, 2005. "Nonrenewable Resource Prices: Deterministic or Stochastic Trends?," Working Papers 05-20, Department of Economics, Appalachian State University.
  • Handle: RePEc:apl:wpaper:05-20
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Pesaran, M. Hashem & Timmermann, Allan, 2005. "Small sample properties of forecasts from autoregressive models under structural breaks," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 183-217.
    2. Hamilton, James D., 1996. "This is what happened to the oil price-macroeconomy relationship," Journal of Monetary Economics, Elsevier, vol. 38(2), pages 215-220, October.
    3. Pesaran, M. Hashem & Timmermann, Allan, 2004. "How costly is it to ignore breaks when forecasting the direction of a time series?," International Journal of Forecasting, Elsevier, vol. 20(3), pages 411-425.
    4. Geoffrey Heal, 1976. "The Relationship Between Price and Extraction Cost for a Resource with a Backstop Technology," Bell Journal of Economics, The RAND Corporation, vol. 7(2), pages 371-378, Autumn.
    5. Robin L. Lumsdaine & David H. Papell, 1997. "Multiple Trend Breaks And The Unit-Root Hypothesis," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 212-218, May.
    6. Berck, Peter & Roberts, Michael, 1996. "Natural Resource Prices: Will They Ever Turn Up?," Journal of Environmental Economics and Management, Elsevier, vol. 31(1), pages 65-78, July.
    7. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    8. Nunes, Luis C & Newbold, Paul & Kuan, Chung-Ming, 1997. "Testing for Unit Roots with Breaks: Evidence on the Great Crash and the Unit Root Hypothesis Reconsidered," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(4), pages 435-448, November.
    9. Peter C.B. Phillips & Sam Ouliaris & Joon Y. Park, 1988. "Testing for a Unit Root in the Presence of a Maintained Trend," Cowles Foundation Discussion Papers 880, Cowles Foundation for Research in Economics, Yale University.
    10. Dimitrios Vougas, 2003. "Reconsidering LM unit root testing," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(7), pages 727-741.
    11. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    12. Ahrens, W. Ashley & Sharma, Vijaya R., 1997. "Trends in Natural Resource Commodity Prices: Deterministic or Stochastic?," Journal of Environmental Economics and Management, Elsevier, vol. 33(1), pages 59-74, May.
    13. Labson B. Stephen & Crompton Paul L., 1993. "Common Trends in Economic Activity and Metals Demand: Cointegration and the Intensity of Use Debate," Journal of Environmental Economics and Management, Elsevier, vol. 25(2), pages 147-161, September.
    14. repec:cup:etheor:v:11:y:1995:i:2:p:359-68 is not listed on IDEAS
    15. Diebold, Francis X & Kilian, Lutz, 2000. "Unit-Root Tests Are Useful for Selecting Forecasting Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 265-273, July.
    16. Lee, Junsoo & Strazicich, Mark C, 2001. " Break Point Estimation and Spurious Rejections with Endogenous Unit Root Tests," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 63(5), pages 535-558, December.
    17. Clark, Todd E. & McCracken, Michael W., 2005. "The power of tests of predictive ability in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 124(1), pages 1-31, January.
    18. Junsoo Lee & Mark C. Strazicich, 2013. "Minimum LM unit root test with one structural break," Economics Bulletin, AccessEcon, vol. 33(4), pages 2483-2492.
    19. Zivot, Eric & Andrews, Donald W K, 2002. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 25-44, January.
    20. Slade, Margaret E., 1988. "Grade selection under uncertainty: Least cost last and other anomalies," Journal of Environmental Economics and Management, Elsevier, vol. 15(2), pages 189-205, June.
    21. Junsoo Lee & Mark C. Strazicich, 2003. "Minimum Lagrange Multiplier Unit Root Test with Two Structural Breaks," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1082-1089, November.
    22. John A. List, 1999. "Have Air Pollutant Emissions Converged Among U.S. Regions? Evidence from Unit Root Tests," Southern Economic Journal, Southern Economic Association, vol. 66(1), pages 144-155, July.
    23. Pindyck, Robert S, 1980. "Uncertainty and Exhaustible Resource Markets," Journal of Political Economy, University of Chicago Press, vol. 88(6), pages 1203-1225, December.
    24. Slade, Margaret E., 1982. "Trends in natural-resource commodity prices: An analysis of the time domain," Journal of Environmental Economics and Management, Elsevier, vol. 9(2), pages 122-137, June.
    25. Harold Hotelling, 1931. "The Economics of Exhaustible Resources," Journal of Political Economy, University of Chicago Press, vol. 39, pages 137-137.
    26. John List, 1999. "Have Air Pollutant Emissions Converged Amongst U.S. Regions?," Natural Field Experiments 00528, The Field Experiments Website.
    27. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    28. Schmidt, Peter & Phillips, C B Peter, 1992. "LM Tests for a Unit Root in the Presence of Deterministic Trends," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 257-287, August.
    29. Diebold, Francis X & Senhadji, Abdelhak S, 1996. "The Uncertain Unit Root in Real GNP: Comment," American Economic Review, American Economic Association, vol. 86(5), pages 1291-1298, December.
    30. Amsler, Christine & Lee, Junsoo, 1995. "An LM Test for a Unit Root in the Presence of a Structural Change," Econometric Theory, Cambridge University Press, vol. 11(02), pages 359-368, February.
    Full references (including those not matched with items on IDEAS)

    More about this item

    JEL classification:

    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:apl:wpaper:05-20. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (O. Ashton Morgan). General contact details of provider: http://edirc.repec.org/data/deappus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.