Scanner Data, Time Aggregation and the Construction of Price Indexes

Abstract

The impact of weekly, monthly and quarterly time aggregation on estimates of price change is examined for nineteen different supermarket item categories over a fifteen month period using scanner data. We find that time aggregation choices (the choice of a weekly, monthly or quarterly unit value concept for prices) have a considerable impact on estimates of price change. When chained indexes are used, the difference in price change estimates can be huge, ranging from 0.28% to 29.73% for a superlative (Fisher) index and an incredible 14.88% to 46,463.71% for a non-superlative (Laspeyres) index. The results suggest that traditional index number theory breaks down when weekly data with severe price bouncing are used, even for superlative indexes. Monthly and (in some cases even) quarterly time aggregation were found to be insufficient to eliminate downward drift in superlative indexes. In order to eliminate chain drift, multilateral index number methods are adapted to provide drift free measures of price change.Price indexes, aggregation, scanner data, chain drift, superlative indexes, unit values, multilateral index number methods, rolling window GEKS, rolli