Quantile Trend Regression and Its Application to Central England Temperature

Harry Haupt and Markus Fritsch
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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

Mathematics, 2022, vol. 10, issue 3, 1-20

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.

Keywords: temperature; trend modeling; seasonality; heterogeneity; quantile regression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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