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Modelling circular time series

Author

Listed:
  • Harvey, Andrew
  • Hurn, Stan
  • Palumbo, Dario
  • Thiele, Stephen

Abstract

Circular variables often play an important role in the construction of models for analysing and forecasting the consequences of climate change and its impact on the environment. Such variables pose special problems for time series modelling. This article shows how the score-driven approach, developed primarily in econometrics, provides a natural solution to the difficulties and leads to a coherent and unified methodology for estimation, model selection and testing. The new methods are illustrated with data on wind direction.

Suggested Citation

  • Harvey, Andrew & Hurn, Stan & Palumbo, Dario & Thiele, Stephen, 2024. "Modelling circular time series," Journal of Econometrics, Elsevier, vol. 239(1).
    • Handle: RePEc:eee:econom:v:239:y:2024:i:1:s0304407623001446
    DOI: 10.1016/j.jeconom.2023.02.016
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    References listed on IDEAS

    as
    1. Stan Hurn & Jing Tian & Lina Xu, 2021. "Assessing the Informational Content of Official Australian Bureau of Meteorology Forecasts of Wind Speed," The Economic Record, The Economic Society of Australia, vol. 97(319), pages 525-547, December.
    2. Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
    3. Jensen, Søren Tolver & Rahbek, Anders, 2004. "Asymptotic Inference For Nonstationary Garch," Econometric Theory, Cambridge University Press, vol. 20(6), pages 1203-1226, December.
    4. Toshihiro Abe & Arthur Pewsey, 2011. "Sine-skewed circular distributions," Statistical Papers, Springer, vol. 52(3), pages 683-707, August.
    5. Arthur Pewsey & Eduardo García-Portugués, 2021. "Rejoinder on: Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 76-82, March.
    6. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    7. Abe, Toshihiro & Ley, Christophe, 2017. "A tractable, parsimonious and flexible model for cylindrical data, with applications," Econometrics and Statistics, Elsevier, vol. 4(C), pages 91-104.
    8. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
    9. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    10. Lagona, Francesco, 2019. "Copula-based segmentation of cylindrical time series," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 16-22.
    11. Shogo Kato, 2010. "A Markov process for circular data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(5), pages 655-672, November.
    12. Jones, M.C. & Pewsey, Arthur, 2005. "A Family of Symmetric Distributions on the Circle," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1422-1428, December.
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    More about this item

    Keywords

    Directional statistics; Dynamic conditional score model; Nonstationarity; von Mises distribution; Wind direction;
    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

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    Access and download statistics

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