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On the Dynamics of Price Discovery: Energy and Agricultural Markets with and without the Renewable Fuels Mandate

Author

Listed:
  • Shiva, Layla
  • Bessler, David A.
  • McCarl, Bruce A.

Abstract

We model the energy–agriculture linkage through structural vector autoregression (VAR) model. This model quantifies the relative importance of various contributing factors in driving prices in both markets. The LiNGAM algorithm from the machine learning literature is used to help identify structural parameters in contemporaneous time. We perform conditional forecasting, taking into account the renewable fuel standards policies, and compare the forecasted path of prices with and without the renewable fuels mandates.

Suggested Citation

  • Shiva, Layla & Bessler, David A. & McCarl, Bruce A., 2014. "On the Dynamics of Price Discovery: Energy and Agricultural Markets with and without the Renewable Fuels Mandate," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169780, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea14:169780
    DOI: 10.22004/ag.econ.169780
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    References listed on IDEAS

    as
    1. Geweke, John & Meese, Richard, 1981. "Estimating regression models of finite but unknown order," Journal of Econometrics, Elsevier, vol. 16(1), pages 162-162, May.
    2. Wallace E. Tyner, 2010. "The integration of energy and agricultural markets," Agricultural Economics, International Association of Agricultural Economists, vol. 41(s1), pages 193-201, November.
    3. Christopher A. Sims, 1982. "Policy Analysis with Econometric Models," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 13(1), pages 107-164.
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    Cited by:

    1. Brorsen, B. Wade, 2017. "2016 WAEA Presidential Address: Comments on Agricultural Economics Research," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 42(01), pages 1-9, January.

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