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Binary quantile regression and variable selection: A new approach

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  • Katerina Aristodemou
  • Jian He
  • Keming Yu

Abstract

In this paper, we propose a new estimation method for binary quantile regression and variable selection which can be implemented by an iteratively reweighted least square approach. In contrast to existing approaches, this method is computationally simple, guaranteed to converge to a unique solution and implemented with standard software packages. We demonstrate our methods using Monte-Carlo experiments and then we apply the proposed method to the widely used work trip mode choice dataset. The results indicate that the proposed estimators work well in finite samples.

Suggested Citation

  • Katerina Aristodemou & Jian He & Keming Yu, 2019. "Binary quantile regression and variable selection: A new approach," Econometric Reviews, Taylor & Francis Journals, vol. 38(6), pages 679-694, July.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:6:p:679-694
    DOI: 10.1080/07474938.2017.1417701
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    Cited by:

    1. G. De Novellis & M. Doretti & G. E. Montanari & M. G. Ranalli & N. Salvati, 2024. "Performance evaluation of nursing homes using finite mixtures of logistic models and M-quantile regression for binary data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 753-781, July.
    2. Cristina Martínez-Gómez & Francisca Jiménez-Jiménez & M. Virtudes Alba-Fernández, 2020. "Determinants of Overfunding in Equity Crowdfunding: An Empirical Study in the UK and Spain," Sustainability, MDPI, vol. 12(23), pages 1-29, December.

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