Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.