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    Random Projection Estimation of Discrete-Choice Models with Large Choice Sets

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    Date
    2018-04-06
    Author
    Chiong, Khai Xiang
    Shum, Matthew
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    Abstract
    Abstract
    We introduce random projection, an important dimension-reduction tool from machine learning, for the estimation of aggregate discrete-choice models with high-dimensional choice sets. Initially, high-dimensional data are projected into a lower-dimensional Euclidean space using random projections. Subsequently, estimation proceeds using cyclical monotonicity moment inequalities implied by the multinomial choice model; the estimation procedure is semiparametric and does not require explicit distributional assumptions to be made regarding the random utility errors. Our procedure is justified via the Johnson-Lindenstrauss lemma-the pairwise distances between data points are preserved through random projections. The estimator works well in simulations and in an application to a supermarket scanner data set.
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    Due to copyright restrictions and/or publisher's policy full text access from Treasures at UT Dallas is limited to current UTD affiliates (use the provided Link to Article).
    URI
    http://dx.doi.org/10.1287/mnsc.2017.2928
    https://hdl.handle.net/10735.1/8756
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