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Quantile Regression: A Methodological Overview

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Quantile Regression for Cross-Sectional and Time Series Data

Part of the book series: SpringerBriefs in Finance ((BRIEFSFINANCE))

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Abstract

In this chapter, the main methodological concepts related to quantile regression are described. We provide the definition of conditional and unconditional quantiles and present the minimization problem with asymmetric loss that underlies the quantile estimation via quantile regressions. Additionally, weighted quantile regression tools that will be used in the following sections of the book are presented at the end of this chapter.

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References

  • Conde Amboage, M., González Manteiga, W., & Sánchez Sellero, C. A. (2018). Quantile regression: Estimation and lack-of-fit tests. BEIO, Boletín de Estadística e Investigación Operativa, 34(2), 97–116.

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Author information

Authors and Affiliations

  1. Faculty of Economics and Business, Open University of Catalonia, Barcelona, Spain

    Jorge M. Uribe

  2. Department of Econometrics, University of Barcelona, Barcelona, Spain

    Montserrat Guillen

Authors
  1. Jorge M. Uribe
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  2. Montserrat Guillen
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Corresponding author

Correspondence to Montserrat Guillen .

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Uribe, J.M., Guillen, M. (2020). Quantile Regression: A Methodological Overview. In: Quantile Regression for Cross-Sectional and Time Series Data. SpringerBriefs in Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-44504-1_3

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