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

Yan, Jin and Yoo, Hong Il (2019) 'Semiparametric estimation of the random utility model with rank-ordered choice data.', Journal of econometrics., 211 (2). pp. 414-438.


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.

Item Type:Article
Full text:Publisher-imposed embargo until 28 March 2021.
(AM) Accepted Manuscript
First Live Deposit - 02 April 2019
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
File format - PDF
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Publisher statement:© 2019 This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Record Created:02 Apr 2019 10:13
Last Modified:13 Jun 2019 10:10

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