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.