A Low-Dimension Shrinkage Approach to Choice-Based Conjoint Estimation
50 Pages Posted: 29 Sep 2020 Last revised: 7 Apr 2021
Date Written: August 12, 2020
Abstract
Estimating consumers' heterogeneous preferences using choice-based conjoint (CBC) data poses a considerable modeling challenge, as the amount of information elicited from each consumer is often limited. Given the lack of individual-level information, effective information pooling across consumers becomes critical for accurate CBC estimation. In this paper, we propose an innovative low-dimension shrinkage approach to pooling information and modeling preference heterogeneity, in which we learn a low-dimensional affine subspace approximation of the heterogeneity distribution and shrink the individual-level part-worth estimates toward this affine subspace. Drawing on recent modeling techniques for low-rank matrix recovery, we develop a computationally tractable machine learning model for implementing this low-dimension shrinkage and apply it to CBC estimation. We use an extensive simulation experiment and a field data set to demonstrate the superior performance of our low-dimension shrinkage approach as compared to alternative benchmark models.
Keywords: Choice-Based Conjoint, Preference Heterogeneity, Low-Dimension Shrinkage, Machine Learning
JEL Classification: M31
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