Abstract:
We estimate a dynamic model of how consumers learn about and choose between different brands of
personal computers (PCs). To estimate the model, we use a panel data set that contains the search and purchase
behavior of a set of consumers who were in the market for a PC. The data includes the information sources visited
each period, search durations, as well as measures of price expectations and stated attitudes toward the alternatives
during the search process. Our model extends recent work on estimation of Bayesian learning models of consumer
choice behavior in environments characterized by uncertainty by estimating a model of active learning—i.e., a
model in which consumers make optimal sequential decisions about how much information to gather prior to
making a purchase. Also, following the suggestion of Manski (2003), we use our data on price expectations to
model consumers’ price expectation process, and, following the suggestion of McFadden (1989a), we incorporate
the stated brand quality information into our likelihood function, rather than modeling only revealed preference
data.
Our analysis sheds light on how consumer forward-looking price expectations and the process of learning about
quality influence the consumer choice process. A key finding is that estimates of dynamic price elasticities of
demand exceed estimates that ignore the expectations effect by roughly 50%. This occurs because our estimated
expectations formation process implies that consumers expect mean reversion in price changes. This enhances
the impact of a temporary price cut. Finally, while our work focuses specifically on the PC market, the modeling
approach we develop here may be useful for studying a wide range of high-tech, high-involvement durable goods
markets where active learning is important.
