Publications
Publications
Embedded Files
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
2. Banks as Firms: The Macroeconomics of Financial Firm Dynamics Job Market Paper
Abstract: Why do certain banks grow large while others remain small, and how does this heterogeneity shape economic activity and policy? To answer this question, I first develop a framework in which banks grow by expanding across regional loan markets and accumulating market share based on differences in screening ability, capital costs, and non-pecuniary loan appeal. I then estimate U.S. bank-level fundamentals using a structurally identified dynamic state-space model and embed these estimates in a quantitative multi-region New Keynesian model. I find that banks' screening ability is the primary driver of bank dynamics: banks that grow large and capture market share are better at screening and pricing risk. This is because, unlike capital costs and loan appeal, screening ability directly affects loan losses and balance-sheet growth. This bank heterogeneity has two macroeconomic consequences: (1) screening ability shapes the allocation of credit across firms, so that changes in screening ability generate short-run fluctuations in aggregate labor productivity, while dispersion across banks lowers its long-run level by roughly 5 percent, and (2) differences in regional banking fundamentals generate the asymmetric transmission of monetary policy, with regions served by weaker banks responding more strongly to a common policy shock.
2. 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. I then jointly estimate the contribution of shocks to bank and household sentiment, aggregate demand and supply, financial risk, and monetary policy to fluctuations in macroeconomic conditions using a structural BVAR framework. Bank sentiment shocks explain 38% of the business cycle variation in credit conditions, 10% in output, 22% in prices, and 26% in the policy rate.
Works in Progress
Works in Progress
Monetary Policy, Financial Vulnerabilities, and Macro Risks
Coauthored with Andrea Ajello
Financial Conditions and the Spatial Distribution of Entrepreneurship
Coauthored with Emin Dinlersoz, Timothy Dunne, John Halitwanger, and Veronika Penciakova
Uncertainty Shocks, Market Concentration, and the Entrepreneurial Funding Channel
Presented at the 2024 SEA meeting in Washington DC
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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