Econometric software packages typically report a fixed number of decimal digits for coefficient estimates and their associated standard errors. This practice misses the opportunity to use rounding rules that convey statistical precision. Using insights from the testing statistical hypotheses of...

Models defined by moment inequalities have become a standard modeling framework for empirical economists, spreading over a wide range of fields within economics. From the point of view of an empirical researcher, the literature on inference in moment inequality models is large and complex,...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

Benchmark finance and macroeconomic models appear to deliver conflicting estimates of the natural rate and bond risk premia. This natural rate puzzle applies not only in the U.S. but across many advanced economies. We use a unified no-arbitrage macro- finance model with two trend factors to...

We conduct a simulation study of Local Projection (LP) and Vector Autoregression (VAR) estimators of structural impulse responses across thousands of data generating processes, designed to mimic the properties of the universe of U.S. macroeconomic data. Our analysis considers various...

The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the...