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Quantile Trend Regression and Its Application to Central England Temperature

Author

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  • Harry Haupt

    (Chair of Statistics and Data Analytics, School of Business, Economics and Information Systems, University of Passau, 94032 Passau, Germany)

  • Markus Fritsch

    (Chair of Statistics and Data Analytics, School of Business, Economics and Information Systems, University of Passau, 94032 Passau, Germany)

Abstract

The identification and estimation of trends in hydroclimatic time series remains an important task in applied climate research. The statistical challenge arises from the inherent nonlinearity, complex dependence structure, heterogeneity and resulting non-standard distributions of the underlying time series. Quantile regressions are considered an important modeling technique for such analyses because of their rich interpretation and their broad insensitivity to extreme distributions. This paper provides an asymptotic justification of quantile trend regression in terms of unknown heterogeneity and dependence structure and the corresponding interpretation. An empirical application sheds light on the relevance of quantile regression modeling for analyzing monthly Central England temperature anomalies and illustrates their various heterogenous trends. Our results suggest the presence of heterogeneities across the considered seasonal cycle and an increase in the relative frequency of observing unusually high temperatures.

Suggested Citation

  • Harry Haupt & Markus Fritsch, 2022. "Quantile Trend Regression and Its Application to Central England Temperature," Mathematics, MDPI, vol. 10(3), pages 1-20, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:413-:d:736508
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    References listed on IDEAS

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    1. Philipp Sibbertsen, 2004. "Long memory versus structural breaks: An overview," Statistical Papers, Springer, vol. 45(4), pages 465-515, October.
    2. Gadea Rivas, María Dolores & Gonzalo, Jesús, 2020. "Trends in distributional characteristics: Existence of global warming," Journal of Econometrics, Elsevier, vol. 214(1), pages 153-174.
    3. Peter A. Stott & Nikolaos Christidis & Friederike E. L. Otto & Ying Sun & Jean‐Paul Vanderlinden & Geert Jan van Oldenborgh & Robert Vautard & Hans von Storch & Peter Walton & Pascal Yiou & Francis W., 2016. "Attribution of extreme weather and climate‐related events," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 7(1), pages 23-41, January.
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    1. Helton Saulo & Roberto Vila & Giovanna V. Borges & Marcelo Bourguignon & Víctor Leiva & Carolina Marchant, 2023. "Modeling Income Data via New Parametric Quantile Regressions: Formulation, Computational Statistics, and Application," Mathematics, MDPI, vol. 11(2), pages 1-25, January.

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