Goods Versus Characteristics: Revealed Preference Procedures for Nested Models
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.
|Date of creation:||01 Feb 2011|
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