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Journal Article
Fiscal Policy and COVID-19: Insights from a Quantitative Model
Unemployment insurance may be the most effective way to help households right now.
Journal Article
“Stress Testing” Banks on Commercial Real Estate
Recent research tests the effects of a large (hypothetical) drop in commercial real estate prices: Banks most affected would be small and the resulting noncompliance would apply to a small fraction of assets in the US banking system.
Working Paper
A Quantitative Theory of Relationship Lending
Banks' loan pricing decisions reflect the fact that borrowers tend to have long-lasting relationships with lenders. Therefore, pricing decisions have an inherently dynamic component: high interest rates may yield higher static profits per loan, but in the long run they erode a banks' customer base and reduce future profitability. We study this tradeoff using a dynamic banking model which embeds lending relationships using deep habits (“customer capital”) and costs of adjusting loan portfolio composition. High customer capital raises the level and decreases the interest rate elasticity of ...
Working Paper
Fiscal Policy during a Pandemic
I study the effects of the 2020 coronavirus outbreak in the United States and subsequent fiscal policy response in a nonlinear DSGE model. The pandemic is a shock to the utility of contact-intensive services that propagates to other sectors via general equilibrium, triggering a deep recession. I use a calibrated version of the model that matches the path of the US unemployment rate in 2020 to analyze different types of fiscal policies. I find that UI benefits are the most effective tool to stabilize income for borrowers, who are the hardest hit, while liquidity assistance programs are ...
The Evolution of Household Net Worth during COVID-19
The U.S. economy suffered a huge shock with the onset of the COVID-19 pandemic, yet asset returns were relatively high during the recovery. Who benefited?
Working Paper
Artificial Intelligence and Inflation Forecasts
We explore the ability of Large Language Models (LLMs) to produce conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor. We argue that this method of generating forecasts is inexpensive and can be ...
Working Paper
Measuring Labor Supply and Demand Shocks during COVID-19
We measure labor demand and supply shocks at the sector level around the COVID-19 outbreak by estimating a Bayesian structural vector autoregression on monthly statistics of hours worked and real wages. Most sectors were subject to large negative labor supply and demand shocks in March and April, with substantial heterogeneity in the size of shocks across sectors. Our estimates suggest that two-thirds of the drop in the aggregate growth rate of hours in March and April 2020 are attributable to labor supply. We validate our estimates of supply shocks by showing that they are correlated with ...
Working Paper
Evergreening
We develop a simple model of relationship lending where lenders have incentives for evergreening loans by offering better terms to less productive and more indebted firms. We detect such lending behavior using loan-level supervisory data for the United States. Low-capitalized banks systematically distort firms’ risk assessments to window-dress their balance sheets. To avoid further reductions in their capital ratios, such banks extend relatively more credit to underreported borrowers. We incorporate the theoretical mechanism into a dynamic heterogeneous-firm model to show that evergreening ...
Journal Article
Rising Interest Rates, the Deficit, and Public Debt
Primary deficits matter more than rising interest rates for public debt.
Working Paper
EBITDA Add-backs in Debt Contracting: A Step Too Far?
Financial covenants in syndicated loan agreements often rely on definitions of EBITDA that deviate from the GAAP definition. We document the increased usage of non-GAAP addbacks toEBITDA in recent times. Using the 2013 Interagency Guidance on Leveraged Lending, which we argue led to an exogenous increase in non-GAAP EBITDA addbacks, we show that these addbacksincrease the likelihood of loan delinquency and default, and also increase the likelihood of the borrower experiencing a ratings downgrade. Greater use of non-GAAP EBITDA addbacks also makes it more likely that lead arrangers lower their ...