Goods Versus Characteristics: Revealed Preference Procedures for Nested Models
AbstractThis 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.
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Bibliographic InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 531.
Date of creation: 01 Feb 2011
Date of revision:
Characteristics; demand; dimension reduction; nested models; revealed preference;
Other versions of this item:
- Matthew Polisson, 2011. "Goods Versus Characteristics: Dimension Reduction and Revealed Preference," Discussion Papers in Economics 11/44, Department of Economics, University of Leicester, revised Dec 2011.
- D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
- D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-02-19 (All new papers)
- NEP-CIS-2011-02-19 (Confederation of Independent States)
- NEP-MKT-2011-02-19 (Marketing)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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