Research

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, 48, 100884, 2021.
MEDIA: AEA Video Interview
[9] “Bank Regulation, Network Topology, and Systemic Risk: Evidence from the Great Depression,” with Sanjiv Das and Kris Mitchener,
  Journal of Money, Credit and Banking, 54, 5, 1261-1312, 2022.
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
NBER Working Paper
Previously titled "Systemic Risk and the Great Depression"
[10] “Digitization and Data Frames for Card Index Records,” with Someswar Amujala and Sanjiv Das,
  Explorations in Economic History, 87, 101469, 2023.
CODE: GitHub
MEDIA: CMC Article
[11] “Signals and Stigmas from Banking Interventions: Lessons from the Bank Holiday in 1933,” with M atthew Jaremski and Gary Richardson,
  Journal of Financial Economics, 163, 104968, 2025.
SUMMARY: NBER Digest
[12] “Fighting ‘Fear Itself’: The Bank Holiday of March 1933” with M atthew Jaremski and Gary Richardson,
  Journal of Financial Crises, 7, 1, 31-56, 2025.

Working Papers

“Stock Volatility and the War Puzzle,” with Marc Weidenmier and Gustavo Cortes, NBER Working paper 29837.

ABSTRACT
- U.S. stock volatility is 33 percent lower during wartime and periods of conflict. This is true even for World Wars I and II, which would seemingly increase uncertainty. In a seminal paper, Schwert (1989) identified the “war puzzle” as one of the most surprising facts from two centuries of stock volatility data. We propose an explanation for the puzzle: the profits of firms become easier to forecast during wartime due to massive government spending. We test this hypothesis using newly-constructed data on more than 100 years of defense spending. The aggregate analysis finds that defense spending reduces stock volatility. The sector level regressions show that defense spending predicts lower stock volatility for firms that produce military goods. Finally, an event-study demonstrates that earnings forecasts of defense firms by equity analysts become significantly less disperse after 9/11 and the invasions of Afghanistan (2001) and Iraq (2003).
MEDIA: New York Times | Financial Times | MarketWatch | The Telegraph | The Australian | Money Control | The Age | National Affairs | Marginal Revolution
SUMMARY: NBER Digest

“How Do Financial Crises Redistribute Risk,” with Kris Mitchener, NBER Working Paper 31537.

ABSTRACT:
- We examine how financial crises redistribute risk, employing novel empirical methods and micro data from the largest financial crisis of the 20th century -- the Great Depression. Using balance-sheet and systemic risk measures at the bank level, we build an econometric model with incidental truncation that jointly considers bank survival, the type of bank closure (consolidations, absorption, and failures), and changes to bank risk. Despite roughly 9,000 bank closures, risk did not leave the financial system; instead, it increased. We show that risk was redistributed to banks that were healthier prior to the financial crisis. A key mechanism driving the redistribution of risk was bank acquisition. Each acquisition increases the balance-sheet and systemic risk of the acquiring bank by 25%. Our findings suggest that financial crises do not quickly purge risk from the system, and that merger policies commonly used to deal with troubled financial institutions during crises have important implications for systemic risk.
MEDIA: MarketWatch | VoxEU Article

“Flexible Bayesian Quantile Analysis of Residential Rental Rates,” with Ivan Jeliazkov, Shubham Karnawat, and Arshad Rahman.

ABSTRACT:
- This article develops a random effects quantile regression model for panel data that allows for increased distributional flexibility, multivariate heterogeneity, and time-invariant covariates in situations where mean regression may be unsuitable. Our approach is Bayesian and builds upon the generalized asymmetric Laplace distribution to decouple the modeling of skewness from the quantile parameter. We derive an efficient simulation-based estimation algorithm, demonstrate its properties and performance in targeted simulation studies, and employ it in the computation of marginal likelihoods to enable formal Bayesian model comparisons. The methodology is applied in a study of U.S. residential rental rates following the Global Financial Crisis. Our empirical results provide interesting insights on the interaction between rents and economic, demographic and policy variables, weigh in on key modeling features, and overwhelmingly support the additional flexibility at nearly all quantiles and across several sub-samples. The practical differences that arise as a result of allowing for flexible modeling can be nontrivial, especially for quantiles away from the median.

“Likelihood Specification in Simultaneous Equation Models for Discrete data,” with Ivan Jeliazkov.

ABSTRACT:
- In a critique on the foundations of a large and diverse literature in economics, we obtain the likelihood function of simultaneous equation models
  for discrete data as the invariant distribution of a suitably defined Markov process. Our formulation provides a well-defined reduced form of the model and
  dispenses with controversial recursivity requirements and the need to augment the data generating process with ad hoc indeterminacy rules. We demonstrate
  that the likelihood is unique, proper, coherent, complete, and theoretically grounded in conditional distribution modeling -- a framework that has yet to
  be popularized in economics. We briefly examine extensions, relevant links, and computational issues, and present an application to a lender-of-last-resort
  program in banking during the Great Depression.

Work in Progress

“A Nested Choice Model of Correspondent Banking Relationships,” with Tanisha Sheth.

“Modeling Through Conditional Distributions,” with Ivan Jeliazkov.