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

Jin Yan and Hong Il Yoo ()

Journal of Econometrics, 2019, vol. 211, issue 2, 414-438

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 heteroskedasticity. With fully rank-ordered data, the GMS estimator becomes considerably more flexible, allowing for random coefficients and alternative-specific heteroskedasticity and correlations. 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.

Keywords: Random utility; Rank-ordered; Discrete choice; Semiparametric estimation; Smoothing (search for similar items in EconPapers)
JEL-codes: C14 C35 (search for similar items in EconPapers)
Date: 2019
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Working Paper: Semiparametric Estimation of the Random Utility Model with Rank-Ordered Choice Data (2017) Downloads
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