In the midst of epidemics such as COVID-19, therapeutic candidates are unlikely to be able to complete the usual multiyear clinical trial and regulatory approval process within the course of an outbreak. We apply a Bayesian adaptive patient-centered model--which minimizes the expected harm of...

We propose an approach to modeling and estimating discrete choice demand that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers then solves...

We revisit time-variation in the Phillips curve, applying new Bayesian panel methods with breakpoints to US and European Union disaggregate data. Our approach allows us to accurately estimate both the number and timing of breaks in the Phillips curve. It further allows us to determine the...

We examine how financial crises redistribute risk, employing novel empirical methods and micro data from the largest financial crisis of the 20th century - the Great Depression. Using balance-sheet and systemic risk measures at the bank level, we build an econometric model with incidental...

This paper uses data from 802,777 veterans assigned to 7,548 primary care providers (PCPs) within the Veterans Health Administration (VHA) to examine variations in the efficacy of primary care providers (PCPs), their consequences for health outcomes, and their determinants. Leveraging...

We measure inequities from the COVID-19 pandemic on mortality and hospitalizations in the United States during the early months of the outbreak. We discuss challenges in measuring health outcomes and health inequality, some of which are specific to COVID-19 and others that complicate attribution...