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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- Martin Browning & Laura Blow, 2006.
"Revealed Preference Analysis of Characteristics Models,"
Economics Series Working Papers
282, University of Oxford, Department of Economics.
- Laura Blow & Martin Browning & Ian Crawford, 2008. "Revealed Preference Analysis of Characteristics Models," Review of Economic Studies, Oxford University Press, vol. 75(2), pages 371-389.
- Laura Blow & Martin Browning & Ian Crawford, 2005. "Revealed preference analysis of characteristics models," CAM Working Papers 2005-04, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
- Laura Blow & Martin Browning & Ian Crawford, 2005. "Revealed preference analysis of characteristics models," School of Economics Discussion Papers 0305, School of Economics, University of Surrey.
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