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

Yan, Jin; Yoo, Hong Il

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Authors

Jin Yan

Hong Il Yoo



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, 2024
Journal Journal of Econometrics
Print ISSN 0304-4076
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

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