Search With Dirichlet Priors: Estimation and Implications for Consumer Demand
为Rothschild(1974)提出的未知分布搜索模型提供了实证应用，开发了狄利克雷先验下最优搜索行为的特征化，并基于共同基金数据估计了搜索模型，发现忽略消费者先验不确定性会导致搜索成本估计严重偏误。
This article is an empirical application of the search model with an unknown distribution, as introduced by Rothschild in 1974. For searchers who hold Dirichlet priors, we develop a novel characterization of optimal search behavior. Our solution delivers easily computable formulas for the ex-ante purchase probabilities as outcomes of search, as required by discrete-choice-based estimation. Using our method, we investigate the consequences of consumer learning on the properties of search-generated demand. Holding search costs constant, the search model from a known distribution predicts larger price elasticities, mainly for the lower-priced products. We estimate a search model with Dirichlet priors, on a dataset of prices and market shares of S&P 500 mutual funds. We find that the assumption of no uncertainty in consumer priors leads to substantial biases in search cost estimates.