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.
翻译:我们提出了一种新的方法,用于研究没有观测到的个体异质环境。根据模型隐蔽的对等不平等,该方法将样本中的个人分类为由无观测到的离散异异异性界定的类别,但支持程度不明。我们确定这些群体在何种条件下被识别并通过我们的方法进行一致估计。我们通过蒙特卡洛模拟显示该方法在有限样本中表现良好。然后我们运用这一方法,利用加利福尼亚高速公路采购市场的数据,估算价格最低的采购拍卖模式,而没有观测到的投标人异异质性。