Goods Versus Characteristics: Dimension Reduction and Revealed Preference

Matthew Polisson

No 11/44, Discussion Papers in Economics from Division of Economics, School of Business, University of Leicester

Abstract: This paper compares the goods and characteristics models of the consumer within a non-parametric revealed preference framework. Of primary interest is to make a comparison on the basis of predictive success that takes into account dimension reduction. This allows us to nonparametrically identify the model which best fits the data. 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.

Keywords: Characteristics; demand; dimension reduction; nested models; revealed preference (search for similar items in EconPapers)
JEL-codes: D11 D12 (search for similar items in EconPapers)
Date: 2011-09, Revised 2011-12
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp11-44.pdf (application/pdf)

Related works:
Working Paper: Goods versus characteristics: dimension reduction and revealed preference (2012)
Working Paper: Goods Versus Characteristics: Revealed Preference Procedures for Nested Models (2011)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:lec:leecon:11/44

Ordering information: This working paper can be ordered from
https://www2.le.ac.u ... -1/discussion-papers

Access Statistics for this paper

More papers in Discussion Papers in Economics from Division of Economics, School of Business, University of Leicester School of Business, University of Leicester, University Road. Leicester. LE1 7RH. UK Provider-Homepage: https://le.ac.uk/school-of-business. Contact information at EDIRC.
Bibliographic data for series maintained by Abbie Sleath ().