Scanner Data and the Treatment of Quality Change in Nonrevisable Price Indexes

Abstract

The recently developed rolling year GEKS procedure makes maximum use of all matches in the data to construct nonrevisable price indexes that are approximately free from chain drift. A potential weakness is that unmatched items are ignored. In this article we use imputation Törnqvist price indexes as inputs into the rolling year GEKS procedure. These indexes account for quality changes by imputing the “missing prices” associated with new and disappearing items. Three imputation methods are discussed. The first method makes explicit imputations using a hedonic regression model which is estimated for each time period. The other two methods make implicit imputations; they are based on time dummy hedonic and time-product dummy regression models and are estimated on bilateral pooled data. We present empirical evidence for New Zealand from scanner data on eight consumer electronics products and find that accounting for quality change can make a substantial difference.

KEY WORDS:

• Hedonic regression
• Imputation
• Multilateral index number methods
• Quality adjustment
• Scanner data
• Transitivity

ACKNOWLEDGMENTS

The authors thank the editor, three anonymous referees, and participants at the following conferences and workshops for very helpful comments on earlier versions of this paper: the UNECE/ILO meeting of the group of experts on consumer price indexes, Geneva, May 2012; Statistics Sweden's workshop on scanner data, Stockholm, June 2012; the New Zealand Association of Economists conference, Palmerston North, June 2012; and the Economic Measurement Group workshop, University of New South Wales, Sydney, November 2012. Thanks also to Alistair Gray and Soon Song for contributions to discussions and empirical work. Financial support from Eurostat (Multi-purpose Consumer Price Statistics Grant) is gratefully acknowledged. The research data were made available by the New Zealand branch of the market research company GfK. The views expressed in this article are those of the authors and do not necessarily reflect the views of Statistics Netherlands or Statistics New Zealand.