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Study of Treatment of Influential Values in a Monthly Retail Trade Survey

Keywords: Outlier, Winsorization, M-estimation

In survey data, an observation is considered influential if it is reported correctly and its weighted contribution has an excessive effect on a key estimate, such as an estimate of total or change. In previous research with data from the U.S. Monthly Retail Trade Survey (MRTS), two methods, Clark Winsorization and weighted M-estimation, have shown potential to detect and adjust influential observations. This paper discusses results of the application of an improved simulation methodology that generates more realistic population time-series data. The new strategy enables evaluating Clark Winsorization and weighted M-estimation over repeated samples and producing conditional and unconditional performance measures. The analyses consider the occurrence of an unusually high influential observation in the MRTS and assess the performance of the two methods for estimates of total retail sales and month-to-month change.