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Forecasting MISO Electricity Prices: A Threshold Autoregressive Approach with Load Data

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  • Tasneem, Faria
  • Waters, George

Abstract

Electricity price dynamics for the Illinois market are examined by estimating eleven different threshold autoregressive models and comparing according to t and forecasting performance. The threshold is endogenous and depends on load data in three of the cases. A theoretical model demonstrates that supply constraints could explain price spikes and that prices would display less persistence in those cases. Estimation results confirm the presence of non-linearity in the evolution of prices. However, inclusion of the load data does not improve performance, which provides evidence against this hypothesis. The model where the threshold depends on the change in the past price is best.

Suggested Citation

  • Tasneem, Faria & Waters, George, 2017. "Forecasting MISO Electricity Prices: A Threshold Autoregressive Approach with Load Data," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 48(3), October.
    • Handle: RePEc:ags:jrapmc:339916
    DOI: 10.22004/ag.econ.339916
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    1. Bowden, Nicholas & Payne, James E., 2008. "Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models," Energy Economics, Elsevier, vol. 30(6), pages 3186-3197, November.
    2. Carlson, J. Lon & Loomis, David, 2008. "An Assessment of the Impact of Deregulation on the Relative Price of Electricity in Illinois," The Electricity Journal, Elsevier, vol. 21(6), pages 60-70, July.
    3. George W. Evans, 2001. "Expectations in Macroeconomics. Adaptive versus Eductive Learning," Revue Économique, Programme National Persée, vol. 52(3), pages 573-582.
    4. Janczura, Joanna & Weron, Rafal, 2010. "An empirical comparison of alternate regime-switching models for electricity spot prices," Energy Economics, Elsevier, vol. 32(5), pages 1059-1073, September.
    5. Payne, James E., 2009. "On the Dynamics of Energy Consumption and Employment in Illinois," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 39(2), pages 1-5.
    6. B. Ricky Rambharat & Anthony E. Brockwell & Duane J. Seppi, 2005. "A threshold autoregressive model for wholesale electricity prices," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(2), pages 287-299, April.
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