University of Notre Dame
Department of Economics
3033 Jenkins Nanovic Hall
Notre Dame, IN 46556
“Borrowing to Save and Investment Dynamics” January 2020.
Revise & Resubmit, Review of Economic Studies
During the U.S. Great Recession, investment declined more among firms whose indebtedness increased. Instead of investing, they increased their leverage and expanded their stock of safe assets; that is, they borrowed to save. I model borrowing to save as an optimal portfolio choice when firms face gradually resolving uncertainty, balancing the desire to invest with the need to prevent default. Embedding this into a quantitative general equilibrium model with heterogeneous firms, I show that this mechanism can simultaneously generate a sharp downturn and a slow recovery in response to a combination of first- and second-moment shocks.
“The Information Driven Financial Accelerator” (with Antonio Falato) November 2020.
Imperfect information in credit markets is a quantitatively important source of macroeconomic fragility. We calibrate a dynamic model with uninformed debt investors. A deterioration in the profit outlook makes investors pessimistic about firm creditworthiness. In turn, firms perceive that debt is underpriced and cut back investment. We show that: 1) the model matches the size and cyclical variation of credit spreads; 2) imperfect information accounts for about half of the spike in spreads and one-fifth of the contraction in aggregate investment during the US financial crisis; 3) the economic costs of imperfect information for firm value and investment are substantial.
Work in Progress
“Biased Bank Expectations: Micro Evidence and Macro Consequences” with Antonio Falato. (Working paper soon)
We examine banks’ expectations about the future performance of their loan portfolios, how they are formed and whether they matter for lending decisions. We construct a novel micro dataset on bank expectations using bank-level responses to a set of special questions in the Senior Loan Officer Opinion Survey on Bank Lending Practices for an annual panel of about one hundred U.S. banks between 2006 and 2019. Our first contribution is to document stylized facts on banks’ expectations and behavioral biases, as measured by the systematic forecast errors made by the banks. Our second contribution is to quantify the macroeconomic implications of the forecasting bias, by developing a dynamic equilibrium model with distorted bank expectations.