We use machine learning methods to examine the power of Treasury term spreads and other financial market and macroeconomic variables to forecast US recessions, vis-à-vis probit regression. In particular we propose a novel strategy for conducting cross-validation on classifiers trained with...

The concentration of risk within financial system is considered to be a source of systemic instability. We propose a theory to explain the structure of the financial system and show how it alters the risk taking incentives of financial institutions. We build a model of portfolio choice and...

Yes, they can. I propose a new method to detect credit booms and busts from multivariate systems -- monetary Bayesian vector autoregressions. When observed credit is systematically higher than credit forecasts justified by real economic activity variables, a positive credit gap emerges. The...

We zero in on the expected returns of long-short portfolios based on 120 stock market anomalies by accounting for (1) effective bid-ask spreads, (2) post-publication effects, and (3) the modern era of trading technology that began in the early 2000s. Net of these effects, the average anomaly's...

Monetary policy uncertainty affects the transmission of monetary policy shocks to longer-term nominal and real yields. For a given monetary policy shock, the reaction of yields is more pronounced when the level of monetary policy uncertainty is low. Primary dealers and other investors adjust...

Drastic public health measures such as social distancing or lockdowns can reduce the loss of human life by keeping the number of infected individuals from exceeding the capacity of the health care system but are often criticized because of the social and the economic cost they entail. We...

This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits o fgeneralized data tempering for “online” estimation...

This paper advocates chaining the decomposition of shocks into contributions from forecast errors to the shock decomposition of the latent vector to better understand model inference about latent variables. Such a double decomposition allows us to gauge the inuence of data on latent variables,...

In spring 2020, the COVID-19 pandemic and related shutdowns had huge effects on unemployment. Using data from the Survey of Consumer Finances, we describe the financial profiles of US families whose workers were most vulnerable to coronavirus-related earnings losses in the spring of 2020, based...

Joblessness is highly seasonal. To analyze how households adapt to seasonal joblessness, we introduce a measure of seasonal work interruptions premised on the idea that a seasonal worker will tend to exit employment around the same time each year. We show that an excess share of prime-age US...