IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i3p413-d736508.html
   My bibliography  Save this article

Quantile Trend Regression and Its Application to Central England Temperature

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/3/413/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/3/413/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Philipp Sibbertsen, 2004. "Long memory versus structural breaks: An overview," Statistical Papers, Springer, vol. 45(4), pages 465-515, October.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Phella, Anthoulla & Gabriel, Vasco J. & Martins, Luis F., 2024. "Predicting tail risks and the evolution of temperatures," Energy Economics, Elsevier, vol. 131(C).
    2. Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and HAR Models," Working Papers 881, Queen Mary University of London, School of Economics and Finance.
    3. Les Oxley & Chris Price & William Rea & Marco Reale, 2008. "A New Procedure to Test for H Self-Similarity," Working Papers in Economics 08/16, University of Canterbury, Department of Economics and Finance.
    4. Gadea Rivas, María Dolores & Olmo, José, 2024. "Testing extreme warming and geographical heterogeneity," UC3M Working papers. Economics 45023, Universidad Carlos III de Madrid. Departamento de Economía.
    5. Richard T. Baillie & Dooyeon Cho & Seunghwa Rho, 2023. "Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility," Empirical Economics, Springer, vol. 64(6), pages 2911-2937, June.
    6. Luis Alberiko Gil-Alana & Zeynel Abidin Ozdemir & Aysit Tansel, 2019. "Long Memory in Turkish Unemployment Rates," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(1), pages 201-217, January.
    7. Canarella, Giorgio & Miller, Stephen M., 2017. "Inflation targeting and inflation persistence: New evidence from fractional integration and cointegration," Journal of Economics and Business, Elsevier, vol. 92(C), pages 45-62.
    8. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    9. Beran, Jan, 2007. "On parameter estimation for locally stationary long-memory processes," CoFE Discussion Papers 07/13, University of Konstanz, Center of Finance and Econometrics (CoFE).
    10. Willert, Juliane, 2009. "Mean Shift detection under long-range dependencies with ART," MPRA Paper 17874, University Library of Munich, Germany.
    11. Sibbertsen, Philipp & Venetis, Ioannis, 2003. "Distinguishing between long-range dependence and deterministic trends," Technical Reports 2003,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    12. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & Poza, Carlos, 2020. "High and low prices and the range in the European stock markets: A long-memory approach," Research in International Business and Finance, Elsevier, vol. 52(C).
    13. Lihong Wang, 2020. "Lack of fit test for long memory regression models," Statistical Papers, Springer, vol. 61(3), pages 1043-1067, June.
    14. Karanasos, Menelaos & Paraskevopoulos, Alexandros & Magdalinos, Anastasios & Canepa, Alessandra, 2024. "A Unified Theory for Arma Models with Varying Coefficients: One Solution Fits All," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202413, University of Turin.
    15. Sibbertsen, Philipp, 2003. "Log-periodogram estimation of the memory parameter of a long-memory process under trend," Statistics & Probability Letters, Elsevier, vol. 61(3), pages 261-268, February.
    16. Choi, Kyongwook & Zivot, Eric, 2007. "Long memory and structural changes in the forward discount: An empirical investigation," Journal of International Money and Finance, Elsevier, vol. 26(3), pages 342-363, April.
    17. Gadea Rivas, María Dolores, 2025. "Global and regional long-term climate forecasts: a heterogeneous future," UC3M Working papers. Economics 45946, Universidad Carlos III de Madrid. Departamento de Economía.
    18. Philipp Sibbertsen & Juliane Willert, 2012. "Testing for a break in persistence under long-range dependencies and mean shifts," Statistical Papers, Springer, vol. 53(2), pages 357-370, May.
    19. Gadea Rivas, María Dolores, 2024. "Regional heterogeneity and warming dominance in the United States," UC3M Working papers. Economics 45017, Universidad Carlos III de Madrid. Departamento de Economía.
    20. Raquel Ayestarán & Juan Infante & Juan José Tenorio & Luis Alberiko Gil-Alana, 2023. "Evidence of Inflation Using Harmonized Consumer Price Indices in Some Euro Countries: France, Germany, Italy, and Spain, along with the Euro Zone," Mathematics, MDPI, vol. 11(10), pages 1-12, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:413-:d:736508. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.