Wittink was a true academic—curious and ready to embrace new ideas and methods—making significant contributions to marketing research and marketing practice. He played an important role in applying econometric methods to marketing problems, such as measuring the impact of advertising, sales promotions, and completion. Wittink also pushed the boundaries of methods like conjoint analysis. He was known for his fair mindedness and ability to look beyond the superficial to evaluate research based on its true merit. Wittink was a mentor and guide to many doctoral students and junior faculty members who benefited tremendously from his input and support.

The Dick Wittink prize is awarded annually to the best paper published in the preceding volume of the QME.

2024 Prize

The Dick Wittink Prize Committee is pleased to announce the 2024 winner of the 18th Annual Dick Wittink prize for the best paper published in the Quantitative Marketing and Economics journal.

Winner

Shrinkage priors for high-dimensional demand estimation

by Adam N. Smith and Jim E. Griffin

Estimating demand for large assortments of differentiated goods requires the specification of a demand system that is sufficiently flexible. However, flexible models are highly parameterized so estimation requires appropriate forms of regularization to avoid overfitting. In this paper, we study the specification of Bayesian shrinkage priors for pairwise product substitution parameters. We use a log-linear demand system as a leading example. Log-linear models are parameterized by own and cross-price elasticities, and the total number of elasticities grows quadratically in the number of goods. Traditional regularized estimators shrink regression coefficients towards zero which can be at odds with many economic properties of price effects. We propose a hierarchical extension of the class of global-local priors commonly used in regression modeling to allow the direction and rate of shrinkage to depend on a product classification tree. We use both simulated data and retail scanner data to show that, in the absence of a strong signal in the data, estimates of price elasticities and demand predictions can be improved by imposing shrinkage to higher-level group elasticities rather than zero.

Past Winners