A simple method to estimate the roles of learning, inventories and category consideration in consumer choice

Models of consumer learning and inventory behavior have both proven to be valuable for explaining consumer choice dynamics. In their pure form these models assume consumers solve complex dynamic programming (DP) problems to determine optimal choices. For this reason, these models are best viewed as "as if" approximations to consumer behavior. In this paper we present an estimation method, based on Geweke and Keane (2000), which allows us to estimate dynamic models without solving a DP problem and without strong assumptions about how consumers form expectations about the future. The relatively low computational burden of this method allows us to nest the learning and inventory models. We also incorporate the "price consideration" mechanism of Ching et al. (2009), which essentially says that consumers may not pay attention to a category in every period. The resulting model may be viewed as providing a more "realistic" or "descriptive" account of consumer choice behavior.