This paper uses over two years of weekly scanner data from two small US 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 even after controlling for heterogeneity across store-product cells and replacing sale prices with regular prices. 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 prices typically reflect the selling store’s recent nominal adjustments rather than changes in other stores’ prices.