Research

Sequential search models provide a powerful framework for studying consumer search using rich data that records the sequence of consumer actions taken during the search process. Existing empirical applications often implement the model based on consumers' optimal policies, in which later decisions depend on outcomes from earlier actions that are not fully observed by researchers. Therefore, implementation is largely restricted by computation burden and limited model flexibility. This paper establishes a theoretical equivalence showing that, under common and mild assumptions of Independence and Invariance, a sequential search process is optimal if and only if a corresponding partial ranking over all feasible actions throughout the process holds, thereby introducing a ranking representation of optimal sequential search. This representation enables a novel, simple, and unified empirical strategy for implementing sequential search models. For the classic Weitzman(1979) model, the proposed approach reduces simulation requirements while improving accuracy, computational efficiency, and ease of implementation. Moreover, the ranking representation and the empirical strategy generalize to a broad class of sequential search settings, including partially observed search data and processes with multi-stage information acquisition, such as discovery. Overall, our results enhance the tractability and the empirical applicability of sequential search models.

One of the most invoked assumptions in economics is that consumers know their preferences when making choices. Although theories and experiments in psychology and behavioral economics suggest that this may be unrealistic, there is relatively little evidence from the field on this question. In this paper, we use detailed clickstream data from a large Central Asian online platform to study the extent to which consumers learn about their preferences while searching for a smartphone. To quantify the speed at which this takes place and account for other factors, most notably that consumers obtain additional product information when they inspect product pages, we estimate a rich search model in which consumers learn about their willingness to pay each time they visit the checkout page. Consumers initially underestimate their price sensitivity and update it along the way. Taking this into account shows that consumers are more price sensitive than a standard search model would predict, and an intervention that prompts consumers to end their search early can lead to potential welfare loss.

We develop a structural sequential search model that incorporates imperfect recall of information acquired during search. Imperfect recall naturally arises in search environments and limits consumers’ ability to make optimal purchase decisions. We identify imperfect recall using consumers’ revisit actions, which allow them to reacquire decayed information, and estimate the model using rich clickstream data from a large online smartphone marketplace. Our results show that imperfect recall substantially influences both consumers' search process and purchase outcomes. Counterfactual simulations indicate that modest reductions in revisit costs, via mechanisms such as bookmarking, comparison tools, or retargeting prompts, can meaningfully reduce suboptimal purchases. These findings highlight the role of imperfect recall in consumer search and provide guidance for interventions that improve consumers' decision quality.

Consumers typically search before making a purchase to resolve product uncertainty under imperfect information. A key factor driving their search decisions is their private evaluation of the product. However, this evaluation often exhibits an endogenous relationship with price, as consumers tend to associate higher prices with better quality. This creates endogeneity between search decisions and product prices beyond the consumer's price sensitivity in purchase. I developed a novel econometric method demonstrating how using instrumental variables can address this endogeneity, enabling accurate estimation of consumers' preferences in purchase.