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Some Novel Aspects of Quantile Regression: Local Stationarity, Random Forests, and Optimal Transportation

In: Recent Advances in Econometrics and Statistics

Author

Listed:
  • Manon Felix

    (University of Geneva, Research Center for Statistics and Geneva School of Economics and Management)

  • Davide La Vecchia

    (University of Geneva, Research Center for Statistics and Geneva School of Economics and Management)

  • Hang Liu

    (University of Science and Technology of China, Faculty of Business in SciTech, School of Management)

  • Yiming Ma

    (University of Science and Technology of China, Department of Statistics and Finance, School of Management)

Abstract

This paper is written for a Festschrift in honor of Professor Marc Hallin, and it proposes some developments on quantile regression. We connect our investigation to Marc’s scientific production, and we present some theoretical and methodological advances for quantile estimation in nonstandard settings. We split our contributions in two parts. The first part is about conditional quantile estimation for nonstationary time series. The second part is about conditional quantile estimation for the analysis of multivariate independent data in the presence of possibly large-dimensional covariates. Monte Carlo studies illustrate numerically the performance of our methods and compare them to some extant techniques.

Suggested Citation

  • Manon Felix & Davide La Vecchia & Hang Liu & Yiming Ma, 2024. "Some Novel Aspects of Quantile Regression: Local Stationarity, Random Forests, and Optimal Transportation," Springer Books, in: Matteo Barigozzi & Siegfried Hörmann & Davy Paindaveine (ed.), Recent Advances in Econometrics and Statistics, pages 261-282, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-61853-6_14
    DOI: 10.1007/978-3-031-61853-6_14
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