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Quantile Trend Regression and Its Application to Central England Temperature

  • 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.

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Metadaten
Author:Harry Haupt, Markus Fritsch
URN:urn:nbn:de:bvb:739-opus4-10382
DOI:https://doi.org/10.3390/math10030413
ISSN:2227-7390
Parent Title (English):Mathematics
Publisher:MDPI
Editor:Vladimir V. Rykov
Document Type:Article
Language:English
Date of first Publication:2022/01/28
Publishing Institution:Universität Passau
Release Date:2022/05/30
Tag:C02; C14; C18; C22; Q54; heterogeneity; quantile regression; seasonality; temperature; trend modeling
Volume:10
Issue:3
Article Number:413
Institutes:Wirtschaftswissenschaftliche Fakultät
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
open_access (DINI-Set):open_access
Funding Acknowledgement:Gefördert durch den Open-Access-Publikationsfonds der Universitätsbibliothek Passau.
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International