We consider a dynamic monetary economy where agents gradually learn about the holding cost of a new asset and coordinate to adopt it as money or abandon it. We provide closed-form solutions for state-contingent asset prices and agents’ adoption decision. The transactional benefits of using money are endogenous and can convexify or concavify the payoff structure. Thus, the arrival of new information can raise or reduce the asset’s price, which is in sharp contrast to standard insights in experimentation models. Full disclosure of the asset type and an increase in the learning speed improve information but have different welfare implications.

We analyse dynamic general equilibrium models with more-or-less directed search by informed buyers and random search by uninformed buyers. This nests existing specifications and generates new insights. A quantitative application concerns the welfare cost of inflation, which is known to be quite high with pure random search and low with pure directed search. Our calibration implies the impact of inflation is fairly low, in part because, in addition to the usual costs, it provides benefits by more heavily taxing high-price sellers that inefficiently profit from exploiting the uninformed. Other applications analyse analytically and numerically changes in credit conditions and information.

We consider an oligopoly model in which consumers engage in sequential search based on partial product information and advertised prices. By applying Weitzman's (1979) optimal sequential search solution, we derive a simple static condition that fully summarizes consumers' shopping outcomes and translates the pricing game among the sellers into a familiar discrete‐choice problem. Exploiting the discrete‐choice reformulation, we provide sufficient conditions that guarantee the existence and uniqueness of market equilibrium and analyze the effects of preference diversity and search frictions on market prices. Among other things, we show that a reduction in search costs raises market prices.