IDEAS home Printed from https://ideas.repec.org/p/fip/fedgif/1173.html
   My bibliography  Save this paper

Oil Price Elasticities and Oil Price Fluctuations

Author

Listed:
  • Dario Caldara
  • Michele Cavallo
  • Matteo Iacoviello

Abstract

We study the identification of oil shocks in a structural vector autoregressive (SVAR) model of the oil market. First, we show that the cross-equation restrictions of a SVAR impose a nonlinear relation between the short-run price elasticities of oil supply and oil demand. This relation implies that seemingly plausible restrictions on oil supply elasticity may map into implausible values of the oil demand elasticity, and vice versa. Second, we propose an identification scheme that restricts these elasticities by minimizing the distance between the elasticities allowed by the SVAR and target values that we construct from a survey of relevant studies. Third, we use the identified SVAR to analyze sources and consequences of movements in oil prices. We find that (1) oil supply shocks and global demand shocks explain 50 and 35 percent of oil price fluctuations, respectively; (2) a drop in oil prices driven by supply shocks boosts economic activity in advanced economies, whereas it depresses economic activity in emerging economies; and (3) the selection of oil market elasticities is essential for understanding the source of oil price movements and to measuring the multipliers of oil prices on economic activity.

Suggested Citation

  • Dario Caldara & Michele Cavallo & Matteo Iacoviello, 2016. "Oil Price Elasticities and Oil Price Fluctuations," International Finance Discussion Papers 1173, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgif:1173
    DOI: 10.17016/IFDP.2016.1173
    as

    Download full text from publisher

    File URL: http://www.federalreserve.gov/econresdata/ifdp/2016/files/ifdp1173.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Mendoza, Enrique G, 1995. "The Terms of Trade, the Real Exchange Rate, and Economic Fluctuations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 36(1), pages 101-137, February.
    2. Kilian, Lutz & Lee, Thomas K., 2014. "Quantifying the speculative component in the real price of oil: The role of global oil inventories," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 71-87.
    3. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    4. repec:ecb:ecbrbu:2018:0051: is not listed on IDEAS
    5. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," Review of Economic Studies, Oxford University Press, vol. 77(2), pages 665-696.
    6. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," Review of Economic Studies, Oxford University Press, vol. 61(4), pages 631-653.
    7. Peersman, Gert & Van Robays, Ine, 2012. "Cross-country differences in the effects of oil shocks," Energy Economics, Elsevier, vol. 34(5), pages 1532-1547.
    8. Francesco Lippi & Andrea Nobili, 2012. "Oil And The Macroeconomy: A Quantitative Structural Analysis," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1059-1083, October.
    9. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
    10. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    11. Pindyck, Robert S & Rotemberg, Julio J, 1990. "The Excess Co-movement of Commodity Prices," Economic Journal, Royal Economic Society, vol. 100(403), pages 1173-1189, December.
    12. Delle Chiaie, Simona & Ferrara, Laurent & Giannone, Domenico, 2018. "Common factors of commodity prices," Research Bulletin, European Central Bank, vol. 51.
    13. Labys, W. C. & Achouch, A. & Terraza, M., 1999. "Metal prices and the business cycle," Resources Policy, Elsevier, vol. 25(4), pages 229-238, December.
    14. Issler, João Victor & Rodrigues, Claudia & Burjack, Rafael, 2014. "Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 310-335.
    15. Luciana Juvenal & Ivan Petrella, 2015. "Speculation in the Oil Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 621-649, June.
    16. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    17. Robert B. Barsky & Lutz Kilian, 2002. "Do We Really Know that Oil Caused the Great Stagflation? A Monetary Alternative," NBER Chapters,in: NBER Macroeconomics Annual 2001, Volume 16, pages 137-198 National Bureau of Economic Research, Inc.
    18. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, April.
    19. Samya Beidas-Strom & Andrea Pescatori, 2014. "Oil Price Volatility and the Role of Speculation," IMF Working Papers 14/218, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hilde C. Bjørnland & Vegard H. Larsen & Junior Maih, 2018. "Oil and Macroeconomic (In)stability," American Economic Journal: Macroeconomics, American Economic Association, vol. 10(4), pages 128-151, October.
    2. Holm-Hadulla, Fédéric & Hubrich, Kirstin, 2017. "Macroeconomic implications of oil price fluctuations: a regime-switching framework for the euro area," Working Paper Series 2119, European Central Bank.
    3. Romain Houssa & Jolan Mohimont & Chris Otrok, 2019. "A Model for International Spillovers to Emerging Markets," CESifo Working Paper Series 7702, CESifo Group Munich.
    4. repec:aea:aecrev:v:109:y:2019:i:5:p:1873-1910 is not listed on IDEAS
    5. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    6. repec:eee:riibaf:v:46:y:2018:i:c:p:149-165 is not listed on IDEAS
    7. Matteo Iacoviello, 2018. "Measuring Geopolitical Risk," 2018 Meeting Papers 79, Society for Economic Dynamics.
    8. repec:eee:enepol:v:129:y:2019:i:c:p:1306-1319 is not listed on IDEAS
    9. Gambetti, Luca & Moretti, Laura, 2017. "News, Noise and Oil Price Swings," Research Technical Papers 12/RT/17, Central Bank of Ireland.
    10. Thomas S. Gundersen, 2018. "The Impact of U.S. Supply Shocks on the Global Oil Price," Working Papers No 7/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    11. repec:zag:zirebs:v:21:y:2018:i:2:p:49-69 is not listed on IDEAS
    12. Ma, Lin, 2018. "Importance of Demand and Supply Shocks for Oil Price Variations," Working Paper Series 10-2018, Norwegian University of Life Sciences, School of Economics and Business.
    13. repec:eee:enepol:v:116:y:2018:i:c:p:357-372 is not listed on IDEAS

    More about this item

    Keywords

    Oil Prices ; Vector Autoregressions ; Commodity Prices;

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:fip:fedgif:1173. 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: (Ryan Wolfslayer) or (Keisha Fournillier). General contact details of provider: http://edirc.repec.org/data/frbgvus.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.