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Scanner Data and Price Indexes$
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Robert C. Feenstra and Matthew D. Shapiro

Print publication date: 2002

Print ISBN-13: 9780226239651

Published to Chicago Scholarship Online: February 2013

DOI: 10.7208/chicago/9780226239668.001.0001

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PRINTED FROM CHICAGO SCHOLARSHIP ONLINE (www.chicago.universitypressscholarship.com). (c) Copyright University of Chicago Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in CHSO for personal use.date: 26 May 2022

The Measurement of Quality-Adjusted Price Changes

The Measurement of Quality-Adjusted Price Changes

Chapter:
(p.277) 9 The Measurement of Quality-Adjusted Price Changes
Source:
Scanner Data and Price Indexes
Author(s):

Mick Silver

Saeed Heravi

Publisher:
University of Chicago Press
DOI:10.7208/chicago/9780226239668.003.0011

This chapter explores the alternative approaches to using scanner data for adjusting prices for quality change. It also compares the results of the three methods of measuring quality-adjusted price changes using scanner data: time dummy variable approach, exact (and superlative) hedonic indexes, and matching procedure. The superlative matched index effectively adjusts for changes in the quality mix of purchases being based on computational matching as opposed to statistical models. The use of hedonic adjustment methods offers an explicit basis for the quality adjustments. Different hedonic adjustment techniques present similar results, although the old and new predicted to actual appear to work best as a geometric mean. Scanner data provide a proxy variable on the extent to which each variety is sold in different outlets, and use of this is being considered to develop the experiment.

Keywords:   scanner data, price changes, quality, time dummy variable approach, superlative matched index, computational matching, hedonic adjustment

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