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An Identification-Robust Test for Time-Varying Parameters in the Dynamics of Energy Prices

Author

Listed:
  • Marie-Claude Beaulieu
  • Jean-Marie Dufour
  • Lynda Khalaf
  • Maral Kichian

Abstract

We test for the presence of time-varying parameters (TVP) in the long-run dynamics of energy prices for oil, natural gas and coal, within a standard class of mean-reverting models. We also propose residual-based diagnostic tests and examine out-of-sample forecasts. In-sample LR tests support the TVP model for coal and gas but not for oil, though companion diagnostics suggest that the model is too restrictive to conclusively fit the data. Out-of-sample analysis suggests a randomwalk specification for oil price, and TVP models for both real-time forecasting in the case of gas and long-run forecasting in the case of coal

Suggested Citation

  • Marie-Claude Beaulieu & Jean-Marie Dufour & Lynda Khalaf & Maral Kichian, 2011. "An Identification-Robust Test for Time-Varying Parameters in the Dynamics of Energy Prices," CIRANO Working Papers 2011s-22, CIRANO.
  • Handle: RePEc:cir:cirwor:2011s-22
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    File URL: https://cirano.qc.ca/files/publications/2011s-22.pdf
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    Citations

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    Cited by:

    1. Murat Midilic, 2016. "Estimation Of Star-Garch Models With Iteratively Weighted Least Squares," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 16/918, Ghent University, Faculty of Economics and Business Administration.
    2. Rachida Ouysse, 2014. "On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models," Computational Statistics, Springer, vol. 29(1), pages 233-261, February.
    3. Bernard, Jean-Thomas & Khalaf, Lynda & Kichian, Maral & Yelou, Clement, 2018. "Oil Price Forecasts For The Long Term: Expert Outlooks, Models, Or Both?," Macroeconomic Dynamics, Cambridge University Press, vol. 22(3), pages 581-599, April.
    4. Murat Midiliç, 2020. "Estimation of STAR–GARCH Models with Iteratively Weighted Least Squares," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 87-117, January.
    5. Maral Kichian, 2012. "Financial Conditions and the Money-Output Relationship in Canada," Staff Working Papers 12-33, Bank of Canada.
    6. Wang, Nan & Mogi, Gento, 2017. "Industrial and residential electricity demand dynamics in Japan: How did price and income elasticities evolve from 1989 to 2014?," Energy Policy, Elsevier, vol. 106(C), pages 233-243.
    7. Zhang, Hui Jun & Dufour, Jean-Marie & Galbraith, John W., 2016. "Exchange rates and commodity prices: Measuring causality at multiple horizons," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 100-120.
    8. Khalaf, Lynda & Saunders, Charles J., 2017. "Monte Carlo forecast evaluation with persistent data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 1-10.
    9. Piersanti, Giovanni & Piersanti, Mirko & Cicone, Antonio & Canofari, Paolo & Di Domizio, Marco, 2020. "An inquiry into the structure and dynamics of crude oil price using the fast iterative filtering algorithm," Energy Economics, Elsevier, vol. 92(C).

    More about this item

    Keywords

    structural change; time-varying parameter; energy prices; coal; gas; crude oil; unidentified nuisance parameter; exact test; Monte Carlo test; Kalman filter; normality test;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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