Binary quantile regression and variable selection: A new approach. (3rd July 2019)

• Record Type: Journal Article
• Authors: Aristodemou, Katerina; He, Jian; Yu, Keming
• Is Part Of: Econometric reviews. Volume 38:Number 6(2019)
• Page Start: 679
• Page End: 694
• Publication Date: 2019-07-03
• Subjects: Adaptive lasso -- binary regression -- iteratively reweighted least squares -- quantile regression -- smoothed maximum score estimator -- variable selection -- work trip mode choice; B23 -- C01 -- C21 -- C31 -- C35; Econometrics -- Periodicals; 330.015195
• DOI: 10.1080/07474938.2017.1417701
• Languages: English
• ISSNs: 0747-4938

Abstract

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.