Publications
[1] “Treatment Effects and Informative Missingness with an Application to Bank Recapitalization Programs,”
American Economic Review (Papers and Proceedings), 104, 5, 212-17, 2014. (Online Appendix)
[2] “Determining the Proper Specification for Endogenous Covariates in Discrete Data Settings,”
Advances in Econometrics, 34, 223-247, 2014.
[3] “Sample Selection and Treatment Effect Estimation of Lender of Last Resort Policies,”
Journal of Business and Economic Statistics, 34, 2, 197-212, 2016.
[4] “The Impact of Estimation Uncertainty on Covariate Effects in Nonlinear Models,” with Ivan Jeliazkov,
Statistical Papers, 59, 3, 1031-1042, 2018.
[5] “Analysis of Stigma and Bank Credit Provision,”
Journal of Money, Credit and Banking, 51, 1, 163-194, 2019.
[6] “The Quality of Banks at Stigmatized Lending Facilities,” with Sriya Anbil,
AEA Papers and Proceedings, 109, 506-510, 2019.
MEDIA: CardRates.com
[7] “Estimation and Applications of Quantile Regression for Binary Longitudinal Data,” with Arshad Rahman,
Advances in Econometrics, 40B, 157-191, 2019.
[8] “Liquidity from Two Lending Facilities,” with Sriya Anbil,
Journal of Financial Intermediation, forthcoming.
MEDIA: AEA Video Interview
Working Papers
“Systemic Risk and the Great Depression,” with Sanjiv Das and Kris Mitchener, NBER Working Paper No. 25405.
“Likelihood Specification in Simultaneous Equation Models for Discrete data,” with Ivan Jeliazkov.
- ABSTRACT:
- Employing unique hand-collected data on correspondent relationships for all U.S. banks and a methodology that captures bank credit risk and network position, we study how roughly 9,000 bank failures altered the network of financial institutions during the Great Depression. We show that the crisis raised systemic risk by 33%, with much of this increase concentrated at the largest banks. The pyramid-like network topology increased the system’s fragility and risk-spreading propensity. Systemic risk increased (decreased) the probability of bank survival for Federal Reserve members (nonmembers), and branch-banking dampened the positive effect on survival stemming from a bank’s central position in the network.
- AWARD: Winner of the WFA's Award for the Best Paper on Financial Institutions (Sponsored by Elsevier, Western Finance Association, 2019). Photo
- MEDIA: VoxEU Article (over 100,000 reads in 3 weeks)
“Likelihood Specification in Simultaneous Equation Models for Discrete data,” with Ivan Jeliazkov.
- ABSTRACT:
- The likelihood function of simultaneous equation models for discrete data is derived as the invariant distribution of a suitably defined Markov chain. The formulation dispenses with controversial recursivity requirements or the need to augment the model with ad hoc indeterminacy rules.
Work in Progress
“A Flexible Bayesian Quantile Regression Analysis of Residential Rental Rates,” with Ivan Jeliazkov, Shubham Karnawat, and Arshad Rahman.
“Specification of Bank Networks and Mergers,” with Sanjiv Das and Kris Mitchener.
“Stock Volatility and the War Puzzle,” with Marc Weidenmier and Gustavo Cortes.
“Modeling Through Conditional Distributions,” with Ivan Jeliazkov.
“Specification of Bank Networks and Mergers,” with Sanjiv Das and Kris Mitchener.
“Stock Volatility and the War Puzzle,” with Marc Weidenmier and Gustavo Cortes.
“Modeling Through Conditional Distributions,” with Ivan Jeliazkov.