Scanner Data, Product Churn and Quality Adjustment

Abstract

High technology products are characterized by the rapid introduction of new models and the corresponding disappearance of older models. The paper addresses the problems associated with the construction of price indexes for these products. Several methods for the quality adjustment of product prices are considered: hedonic regressions that use either product characteristics (Time Dummy Characteristics regressions) or the product itself as the ultimate characteristic (Time Product Dummy regressions). The paper also considered regressions where the economic importance of products is taken into account (weighted versus unweighted regressions). The indexes which were generated by the hedonic regressions were compared to traditional index numbers that did not make any special adjustments for quality change. Finally, the Expanding Window variant of a Weighted Time Product Dummy regression was used to deal with the chain drift problem. The various approaches were implemented using Japanese price and quantity data on laptop sales in Japan for the 24 months over the years 2020-2021.

Keywords: Quality adjustment, scanner data, hedonic regressions, expanding window approach, the chain drift problem, index number theory.