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.