Job Market Paper
Job Market Paper
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Banks as Firms: The Macroeconomics of Financial Firm Dynamics
Abstract: Why do certain banks grow large while others remain small, and how do these dynamics shape economic activity and policy? I first develop a model in which banks grow by expanding across regional loan markets and capturing market share based on differences in their ability to screen risk, attract borrowers, and source capital. Guided by this theory, I estimate U.S. bank-level fundamentals using a dynamic state-space model and embed the estimates in a quantitative multi-region New Keynesian setting. I find that banks' screening ability is the primary driver of bank dynamics, accounting for three-quarters of a bank's idiosyncratic loan growth. This is because, unlike other fundamentals, screening ability directly affects loan losses, which can trigger an accelerating balance-sheet contraction. These dynamics shape the macroeconomy through two channels. First, the distribution of screening ability determines how effectively credit is allocated to productive firms. Changes in screening ability therefore generate short-run fluctuations in aggregate labor productivity, while the level and dispersion of screening ability lower U.S. long-run labor productivity by roughly 1 and 5 percent, respectively. Second, heterogeneity in bank fundamentals generates asymmetric regional transmission of monetary policy, creating a tradeoff between aggregate stabilization and regional inequality.
Publications
Publications
Bank Lending Standards and the U.S. Economy
Journal of Economic Dynamics and Control, 2026
Coauthored with Elijah Broadbent, Huberto Ennis, and Horacio Sapriza
Abstract: The provision of bank credit to firms and households affects macroeconomic performance. We use survey measures of changes in bank lending standards, disaggregated by loan category, to quantify the effect of changes in banks' attitudes toward lending on aggregate output, inflation, and interest rates. Bank lending to businesses is particularly important for macroeconomic outcomes, with peak effects on output of around half a percentage point after four quarters of the initial shock. These effects depend on the stage of the business cycle and the proximity of the short-term interest rate to its effective lower bound. The effects are larger when output is growing below trend and when the interest rate is away from its lower bound. We also find that the response of the economy to lending-standards shocks is asymmetric, with tightening shocks having larger effects on output.
Working Papers
Working Papers
1. Banks, Sentiments, and Business Cycles (Submitted)
Abstract: This paper measures the animal spirits of U.S. banks and asks whether they are an important determinant of credit conditions and source of business cycle fluctuations. I first construct a novel semi-structural measure of bank-level sentiment, revealing heterogeneous animal spirits across banks and common dynamics marked by surges in pessimism during crises and excessive optimism during periods of elevated asset prices. Using a structural BVAR that identifies bank sentiment alongside standard macroeconomic shocks, I find that bank sentiment shocks account for 24% of fluctuations in output growth and 20% in the policy rate. However, effects are time-varying and most acute in recessions when sentiment becomes a dominant source of volatility in real activity and financial conditions. These effects arise as pessimistic sentiment shocks act like a negative credit supply shock propagating primarily through the business lending channel, raising borrowing costs and tightening lending standards. Monetary policy can only partially offset these effects.
2. Uncertainty, Market Concentration, and the Entrepreneurial Funding Channel
Abstract: This paper studies why uncertainty shocks have persistent effects on economic activity. I first document that shocks to uncertainty about non-financial productivity depress firm entry and raise market concentration for longer than standard transmission mechanisms can account for. I then develop a macroeconomic model with a new entrepreneurial funding channel, in which risk-averse investors become less willing to finance prospective entrepreneurs when uncertainty rises. Firm entry falls in response, creating a missing cohort of firms. Because there is no compensating rise in entry when uncertainty subsides, the missing cohort persists and the number of firms recovers only slowly. As a result, output is persistently dampened and market concentration among incumbent firms rises. Transitory uncertainty shocks can therefore generate long-lived changes in the size of the firm population and in market structure.
Works in Progress
Works in Progress
Monetary Policy, Financial Vulnerabilities, and Macro Risks
Coauthored with Andrea Ajello
Local Financial Shocks and Business Formation: Entry, Startup Scale, and Growth
Coauthored with Emin Dinlersoz, Timothy Dunne, John Haltiwanger, and Veronika Penciakova
Pre-Doctoral Research
Pre-Doctoral Research
Out-of-Sample Performance of Recession Probability Models
Coauthored with Francisco Vazquez-Grande
Abstract: This note discusses the out-of-sample (OOS) performance of several probit models used to assess the likelihood that the U.S. economy will be in a recession within the following year.
FEDS NoteCombining Forecasts: Can Machines Beat the Average?
Coauthored with Francisco Vazquez-Grande
Abstract: Yes. This paper documents the benefits of combining forecasts using weights built with non-linear models. We introduce our tree-based forecast combinations and compare them with benchmark equal weight combination as well as other nonlinear forecast weights. We find that nonlinear models can improve consistently upon the equal weight alternative–breaking the so-called ``forecast combination puzzle’’–and that our proposed methods compete well with other nonlinear methods.
Working paper | GitHubBottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults Using Machine Learning
Coauthored with Horacio Sapriza and Tom Zimmermann
Abstract: This paper constructs a leading macroeconomic indicator from microeconomic data using recent machine learning techniques. Using tree-based methods, we estimate probabilities of default for publicly traded non-financial firms in the United States. We then use the cross-section of out-of-sample predicted default probabilities to construct a leading indicator of non-financial corporate health. The index predicts real economic outcomes such as GDP growth and employment up to eight quarters ahead. Impulse responses validate the interpretation of the index as a measure of financial stress.
FEDS Paper | GitHub
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