Treatment Effects and Informative Missingness with an Application to Bank Recapitalization Programs
- (pp. 212-17)
AbstractThis 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.
Citation2014. "Treatment Effects and Informative Missingness with an Application to Bank Recapitalization Programs." American Economic Review, 104(5): 212-17. DOI: 10.1257/aer.104.5.212
- C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- G21 Banks; Depository Institutions; Micro Finance Institutions; Mortgages
- G28 Financial Institutions and Services: Government Policy and Regulation
- N22 Economic History: Financial Markets and Institutions: U.S.; Canada: 1913-