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Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness

N. Gusarov, A. Talebijmalabad and I. Joly

Working Papers from Grenoble Applied Economics Laboratory (GAEL)

Abstract: This work is a cross-disciplinary study of econometrics and machine learning (ML) models applied to consumer choice preference modelling. To bridge the interdisciplinary gap, a simulation and theorytesting framework is proposed. It incorporates all essential steps from hypothetical setting generation to the comparison of various performance metrics. The flexibility of the framework in theory-testing and models comparison over economics and statistical indicators is illustrated based on the work of Michaud, Llerena and Joly (2012). Two datasets are generated using the predefined utility functions simulating the presence of homogeneous and heterogeneous individual preferences for alternatives’ attributes. Then, three models issued from econometrics and ML disciplines are estimated and compared. The study demonstrates the proposed methodological approach’s efficiency, successfully capturing the differences between the models issued from different fields given the homogeneous or heterogeneous consumer preferences.

Keywords: DISCRETE CHOICE MODELS; NEURAL NETWORK; PERFORMANCE COMPARISON; HETEREGENEOUS PREFERENCES (search for similar items in EconPapers)
JEL-codes: C25 C45 C52 C80 C90 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dcm, nep-ecm, nep-ore and nep-upt
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Persistent link: https://EconPapers.repec.org/RePEc:gbl:wpaper:2020-12

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