Semiparametric estimation of the random utility model with rank-ordered choice data

Yan, Jin; Yoo, Hong Il

Abstract

We propose semiparametric methods for estimating random utility models using rank-ordered choice data. Our primary method is the generalized maximum score (GMS) estimator. With partially rank-ordered data, the GMS estimator allows for arbitrary forms of interpersonal het- eroskedasticity. With fully rank-ordered data, the GMS estimator becomes considerably more exible, allowing for random coecients and alternative-specic heteroskedasticity and correla- tions. The GMS estimator has a non-standard asymptotic distribution and a convergence rate of N−1/3. We proceed to construct its smoothed version which is asymptotically normal with a faster convergence rate of N−d/(2d+1), where d 2 increases in the strength of smoothness assumptions.

Citation

Yan, J., & Yoo, H. I. (2019). Semiparametric estimation of the random utility model with rank-ordered choice data. Journal of Econometrics, 211(2), 414-438. https://doi.org/10.1016/j.jeconom.2019.03.003

• Journal Article Type: Article
• Acceptance Date: Mar 15, 2019
• Online Publication Date: Mar 28, 2019
• Publication Date: Aug 31, 2019
• Deposit Date: Apr 2, 2019
• Publicly Available Date: Mar 28, 2021
• Journal: Journal of Econometrics
• Print ISSN: 0304-4076
• Electronic ISSN: 1872-6895
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 211
• Issue: 2
• Pages: 414-438
• DOI: https://doi.org/10.1016/j.jeconom.2019.03.003
• Public URL: https://durham-repository.worktribe.com/output/1299551