We use large unpublished data set about the prices by store of 381 products collected by the Israeli bureau of statistics during 1991-92 in the process of computing the CPI. On average 24% of the stores changed * This paper benefited from comments provided by the participants of the workshop at the Chicago Fed and by comments provided by Jeff Campbell.

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This article uses weekly scanner data from two small U.S. cities to characterize time and state dependence of grocers' pricing decisions. In these data, the probability of a nominal adjustment declines with the time since the last price change. A store's price for a particular product typically goes through several price changes in rapid succession before settling down. We also detect state dependence: The probability of a nominal adjustment is highest when a store's price substantially differs from the average of other stores' prices. However, extreme relative prices typically reflect the store's recent changes instead of changes in average prices.