We develop a method to identify the individual latent propensity to select into treatment and marginal treatment effects. Identification is achieved with survey data on individuals' subjective expectations of their treatment propensity and of their treatment-contingent outcomes. We use the...

In this paper we use the functional vector autoregression (VAR) framework of Chang, Chen, and Schorfheide (2024) to study the effects of monetary policy shocks (conventional and informational) on the cross-sectional distribution of U.S. earnings (from the Current Population Survey), consumption,...

We study the effects of uncertainty on time use and their macroeconomic implications. Employing data from the American Time Use Survey and the Bureau of Labor Statistics, we document that heightened uncertainty increases housework and reduces market work hours, mildly impacting leisure. We then...

Monetary policy is conventionally understood to influence labor demand, with little effect on labor supply. We estimate the response of labor market flows to high-frequency changes in interest rates around FOMC announcements and Fed Chair speeches and find that, in contrast to the consensus...

Using a novel firm-level remote work measure created from big data on Internet activity, we show that firms with higher remote work during the pandemic are more likely to see their employees becoming entrepreneurs. This effect holds both unconditionally and relative to other types of job...

Many have argued that digital technologies such as smartphones and social media are addictive. We develop an economic model of digital addiction and estimate it using a randomized experiment. Temporary incentives to reduce social media use have persistent effects, suggesting social media are...

This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in...

In this paper, we provide a suite of tools for empirical market design, including optimal nonlinear pricing in intensive-margin consumer demand, as well as a broad class of related adverse-selection models. Despite significant data limitations, we are able to derive informative bounds on demand...

VARs are often estimated with Bayesian techniques to cope with model dimensionality. The posterior means define a class of shrinkage estimators, indexed by hyperparameters that determine the relative weight on maximum likelihood estimates and prior means. In a Bayesian setting, it is natural to...

Robustness checks, such as adding controls or sample splits, are a standard feature of reduced-form empirical research. Because of computational costs of reestimating alternative models, they are much less common in structural research using simulation-based methods. We propose a simple...