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Disentangling Demand And Supply Shocks In The Crude Oil Market: How To Check Sign Restrictions In Structural Vars

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  • Helmut Lütkepohl
  • Aleksei NetŠunajev

Abstract

SUMMARY Sign restrictions have become increasingly popular for identifying shocks in structural vector autoregressive (SVAR) models. So far there are no techniques for validating the shocks identified via such restrictions. Although in an ideal setting the sign restrictions specify shocks of interest, sign restrictions may be invalidated by measurement errors, data adjustments or omitted variables. We model changes in the volatility of the shocks via a Markov switching (MS) mechanism and use this device to give the data a chance to object to sign restrictions. The approach is illustrated by considering a small model for the market of crude oil. Earlier findings that oil supply shocks explain only a very small fraction of movements in the price of oil are confirmed and it is found that the importance of aggregate demand shocks for oil price movements has declined since the mid 1980s. Copyright © 2013 John Wiley & Sons, Ltd.

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  • Helmut Lütkepohl & Aleksei NetŠunajev, 2014. "Disentangling Demand And Supply Shocks In The Crude Oil Market: How To Check Sign Restrictions In Structural Vars," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 479-496, April.
  • Handle: RePEc:wly:japmet:v:29:y:2014:i:3:p:479-496
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    Cited by:

    1. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with heteroskedasticity: A review of different volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 2-18.
    2. Herwartz, Helmut & Plödt, Martin, 2014. "Sign restrictions and statistical identification under volatility breaks -- Simulation based evidence and an empirical application to monetary policy analysis," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100326, Verein für Socialpolitik / German Economic Association.
    3. Bataa, Erdenebat & Izzeldin, Marwan & Osborn, Denise R., 2016. "Changes in the global oil market," Energy Economics, Elsevier, vol. 56(C), pages 161-176.
    4. van de Ven, Dirk Jan & Fouquet, Roger, 2017. "Historical energy price shocks and their changing effects on the economy," Energy Economics, Elsevier, vol. 62(C), pages 204-216.
    5. Fabio Santeramo, 2015. "A cursory review of the identification strategies," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 3(1), pages 1-8, December.
    6. Daniele Valenti, 2018. "Modelling the Global Price of Oil: Is there any Role for the Oil Futures-spot Spread?," Working Papers 2018.06, Fondazione Eni Enrico Mattei.
    7. Han, Xu, 2015. "Tests for overidentifying restrictions in Factor-Augmented VAR models," Journal of Econometrics, Elsevier, vol. 184(2), pages 394-419.
    8. Xian, Hui & Colson, Gregory & Karali, Berna & Wetzstein, Michael, 2017. "Do nonrenewable-energy prices affect renewable-energy volatility? The case of wood pellets," Journal of Forest Economics, Elsevier, vol. 28(C), pages 42-48.
    9. repec:eee:dyncon:v:84:y:2017:i:c:p:43-57 is not listed on IDEAS
    10. Jadidzadeh, Ali & Serletis, Apostolos, 2017. "How does the U.S. natural gas market react to demand and supply shocks in the crude oil market?," Energy Economics, Elsevier, vol. 63(C), pages 66-74.
    11. Puonti, Päivi, 2016. "Fiscal multipliers in a structural VEC model with mixed normal errors," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 144-154.
    12. Bao H. NGUYEN & OKIMOTO Tatsuyoshi, 2017. "Asymmetric Reactions of the U.S. Natural Gas Market and Economic Activity," Discussion papers 17102, Research Institute of Economy, Trade and Industry (RIETI).
    13. Lanne, Markku & Meitz, Mika & Saikkonen, Pentti, 2017. "Identification and estimation of non-Gaussian structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 196(2), pages 288-304.
    14. Nautz, Dieter & Netsunajew, Aleksei & Strohsal, Till, 2017. "The Anchoring of Inflation Expectations in the Short and in the Long Run," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168075, Verein für Socialpolitik / German Economic Association.
    15. Yin, Libo & Zhou, Yimin, 2016. "What drives long-term oil market volatility? Fundamentals versus Speculation," Economics Discussion Papers 2016-2, Kiel Institute for the World Economy (IfW).
    16. Dmitry Kulikov & Aleksei Netsunajev, 2016. "Identifying Shocks in Structural VAR models via heteroskedasticity: a Bayesian approach," Bank of Estonia Working Papers wp2015-8, Bank of Estonia, revised 19 Feb 2016.
    17. repec:eee:macchp:v2-415 is not listed on IDEAS
    18. Herwartz, Helmut & Plödt, Martin, 2016. "The macroeconomic effects of oil price shocks: Evidence from a statistical identification approach," Journal of International Money and Finance, Elsevier, vol. 61(C), pages 30-44.

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    JEL classification:

    • 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
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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