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Currently, large-scale sales data for consumer goods, called scanner data,are obtained by scanning the bar codes of individual products at the points ofsale of retail outlets. Many national statistical offices use scanner data tobuild consumer price statistics. In this process, as in other statisticalprocedures, the detection of abnormal transactions in sales prices is animportant step in the analysis. Popular methods for conducting such outlierdetection are the quartile method, the Hidiroglou-Berthelot method, theresistant fences method, and the Tukey algorithm. These methods are basedsolely on information about price changes and not on any of the othercovariates (e.g., sales volume or types of retail shops) that are alsoavailable from scanner data. In this paper, we propose a new method to detectabnormal price changes that takes into account an additional covariate, namely,sales volume. We assume that the variance of the log of the price change is asmooth function of the sales volume and estimate the function from previouslyobserved data. We numerically show the advantages of the new method overexisting methods. We also apply the methods to real scanner data collected atweekly intervals by the Korean Chamber of Commerce and Industry between 2013and 2014 and compare their performance.

Covariate-dependent control limits for the detection of abnormal price changes in scanner data