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Goods versus characteristics: dimension reduction and revealed preference

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  • Matthew Polisson

    ()
    (Institute for Fiscal Studies and University of Oxford)

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.

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Bibliographic Info

Paper provided by Institute for Fiscal Studies in its series IFS Working Papers with number W12/02.

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Date of creation: Mar 2012
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Handle: RePEc:ifs:ifsewp:12/02

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Keywords: Characteristics; demand; dimension reduction; nested models; revealed preference.;

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  1. 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.
  2. M.J. Todd & A. Fostel & H.E. Scarf, 2004. "Two New Proofs of Afriat's Theorem," Econometric Society 2004 North American Summer Meetings 632, Econometric Society.
  3. Matthew Polisson & John Quah, 2012. "Revealed Preference in a Discrete Consumption Space," Discussion Papers in Economics 12/02, Department of Economics, University of Leicester, revised Feb 2012.
  4. Andrew Leicester & Zo� Oldfield, 2009. "Using Scanner Technology to Collect Expenditure Data," Fiscal Studies, Institute for Fiscal Studies, vol. 30(Special I), pages 309-337, December.
  5. Tim Beatty & Ian Crawford, 2010. "How demanding is the revealed preference approach to demand," CeMMAP working papers CWP17/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  6. Rachel Griffith & Lars Nesheim, 2010. "Estimating households' willingness to pay," CeMMAP working papers CWP24/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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