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.