Treatment Effects and Informative Missingness with an Application to Bank Recapitalization Programs
提出一个贝叶斯框架来估计存在样本选择的多变量处理效应模型，并应用于评估最后贷款人政策对银行复苏的效果，使用重建金融公司的银行级数据。
This article develops a Bayesian framework for estimating multivariate treatment effect models in the presence of sample selection. The methodology is applied to a banking study that evaluates the effectiveness of lender of last resort (LOLR) policies and their ability to resuscitate the financial system. This paper employs a novel bank-level dataset from the Reconstruction Finance Corporation, and jointly models a bank's decision to apply for a loan, the LOLR's decision to approve the loan, and the bank's performance a few years after the disbursements. This framework offers practical estimation tools to unveil new answers to important regulatory questions.