Estimating unobserved individual heterogeneity using pairwise comparisons
Estimating unobserved individual heterogeneity using pairwise comparisons
复制标题
使用成对比较估计未观察到的个体异质性
DOI：
10.1016/j.jeconom.2020.11.009
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Xun Tang
中科院分区：
文献类型：
--
作者：
E. Krasnokutskaya;Kyungchul Song;Xun Tang
我们提出了一种研究具有未观察到的个体异质性的环境的新方法。基于模型隐含的成对不等式，该方法将样本中的个体分类为具有未知支持度的离散未观测异质性所定义的组。我们建立的条件下，通过我们的方法确定和一致的估计组。我们通过Monte Carlo模拟表明，该方法在有限样本下表现良好。然后，我们应用该方法来估计一个模型的最低价格采购拍卖与未观察到的投标人的异质性，从加州高速公路采购市场的数据。
We propose a new method for studying environments with unobserved individual heterogeneity. Based on model-implied pairwise inequalities, the method classifies individuals in the sample into groups defined by discrete unobserved heterogeneity with unknown support. We establish conditions under which the groups are identified and consistently estimated through our method. We show that the method performs well in finite samples through Monte Carlo simulation. We then apply the method to estimate a model of lowest-price procurement auctions with unobserved bidder heterogeneity, using data from the California highway procurement market.