序贯搜索模型：一种成对最大秩方法
Sequential Search Models: A Pairwise Maximum Rank Approach
摘要：
本文研究的序贯搜索模型（1）包含未观察到的产品质量，这可以与内生的可观察特征（如价格）和内生的搜索成本变量（如在线搜索中介中的产品排名）相关;（2）不需要研究人员知道消费者和产品之间的匹配值的真实分布。估计这种模型的似然方法给出了有偏差的结果。因此，我提出了一个新的估计-成对最大秩（PMR）估计-偏好和搜索成本参数。我表明，PMR估计是一致的，只使用数据对消费者的搜索顺序之间的一对产品，而不是消费者的充分考虑集或最终购买的数据。此外，我们可以使用PMR估计量来测试数据中的真实匹配值分布。在实证应用中，我应用PMR估计量的影响，在Expedia酒店搜索使用两个样本的数据集，消费者被随机分配。我发现位置效应为0.11 - 0.36，并且使用随机生成排名的样本估计的效应接近使用内生排名的样本估计的效应。此外，我发现数据中真正的匹配值分布不太可能是N（0，1）。忽略内点的似然估计给出了至少1.17的向上偏差;将匹配值分布误指定为N（0，1）给出了至少2.99的向上偏差。
英文摘要：
This paper studies sequential search models that (1) incorporate unobserved product quality, which can be correlated with endogenous observable characteristics (such as price) and endogenous search cost variables (such as product rankings in online search intermediaries); and (2) do not require researchers to know the true distribution of the match value between consumers and products. A likelihood approach to estimate such models gives biased results. Therefore, I propose a new estimator -- pairwise maximum rank (PMR) estimator -- for both preference and search cost parameters. I show that the PMR estimator is consistent using only data on consumers' search order among one pair of products rather than data on consumers' full consideration set or final purchase. Additionally, we can use the PMR estimator to test for the true match value distribution in the data. In the empirical application, I apply the PMR estimator to quantify the effect of rankings in Expedia hotel search using two samples of the data set, to which consumers are randomly assigned. I find the position effect to be \$0.11-\$0.36, and the effect estimated using the sample with randomly generated rankings is close to the effect estimated using the sample with endogenous rankings. Moreover, I find that the true match value distribution in the data is unlikely to be N(0,1). Likelihood estimation ignoring endogeneity gives an upward bias of at least \$1.17; misspecification of match value distribution as N(0,1) gives an upward bias of at least \$2.99.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
