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Semiparametric Estimation of the Random Utility Model with Rank-Ordered Choice Data

Jin Yan () and Hong Il Yoo ()
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Jin Yan: The Chinese University of Hong Kong.

No 2017_02, Working Papers from Durham University Business School

Abstract: We propose two semiparametric methods for estimating the random utility model using rank-ordered choice data. The framework is “semiparametric” in that the utility index includes finite dimensional preference parameters but the error term follows an unspecified distribution. Our methods allow for a flexible form of heteroskedasticity across individuals. With complete preference rankings, our methods also allow for heteroskedastic and correlated errors across alternatives, as well as a variety of random coefficients distributions. The baseline method we develop is the generalized maximum score (GMS) estimator, which is strongly consistent but follows a non-standard asymptotic distribution. To facilitate statistical inferences, we make extra regularity assumptions and develop the smoothed GMS estimator, which is asymptotically normal. Monte Carlo experiments show that our estimators perform favorably against popular parametric estimators under a variety of stochastic specifications

Keywords: Rank-ordered; Random utility; Semiparametric estimation; Smoothing (search for similar items in EconPapers)
JEL-codes: C14 C35 (search for similar items in EconPapers)
Date: 2017-04
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-upt
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Journal Article: Semiparametric estimation of the random utility model with rank-ordered choice data (2019) Downloads
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