Goods Versus Characteristics: Revealed Preference Procedures for Nested Models

Matthew Polisson (Oxford)

IFS Seminar

This paper compares the goods and characteristics models of the consumer

within a traditional demand framework. We examine the nonparametric

revealed preference conditions for the goods and characteristics models,

and we develop a methodology for testing nested models of this class

using nonparametric revealed preference techniques. Of primary interest

is to make a comparison on the basis of predictive success, which

requires that we develop a method to relate set predictions across

models. This allows us to nonparametrically identify the model which

best fits the data, and in doing so, to identify the value added by the

characteristics structure in explaining consumer behavior. We then

explore the effects of hypothetical price variation as implied by our

findings in order to nonparametrically bound any comparative statics of

interest. We implement these procedures on household panel data from the

UK milk market. The primary result is that the better fit of the

characteristics model is entirely attributable to dimension reduction.