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Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments

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Author Info
Arora, Neeraj
Huber, Joel
Abstract

We propose aggregate customization as an approach to improve individual estimates using a hierarchical Bayes choice model. Our approach involves the use of prior estimates to build a common design customized for the average respondent. We conduct two simulation studies to investigate conditions that are most conducive to aggregate customization. The simulations are validated by a field study showing that aggregate customization results in better estimates of individual parameters and more accurate predictions of individuals' choices. The proposed approach is easy to use, and a simulation study can assess the expected benefit from aggregate customization prior to its implementation. Copyright 2001 by the University of Chicago.

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Publisher Info
Article provided by University of Chicago Press in its journal Journal of Consumer Research.

Volume (Year): 28 (2001)
Issue (Month): 2 (September)
Pages: 273-83
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Handle: RePEc:ucp:jconrs:v:28:y:2001:i:2:p:273-83

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  1. Sándor, Z. & Wedel, M., 2003. "Differentiated Bayesian Conjoint Choice Designs," Research Paper ERS-2003-016-MKT Revision, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni. [Downloadable!]
  2. Silvia Ferrini & Riccardo Scarpa, 2005. "Experimental Designs for Environmental Valuation with Choice-Experiments: A Monte-Carlo Investigation," Working Papers in Economics 05/08, University of Waikato, Department of Economics. [Downloadable!]
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