Multinational firms (MNEs) dominate trade flows, yet their global production decisions are often ignored in firm-level studies of exporting and importing. Using newly merged data on US firms' trade and multinational activity by country, we show that MNEs are more likely to trade not only with...

We propose a novel time-series econometric framework to forecast U.S. Presidential election outcomes in real time by combining polling data, economic fundamentals, and political prediction market prices. Our model estimates the joint dynamics of voter preferences across states. Applying our...

How can we use the novel capacities of large language models (LLMs) in empirical research? And how can we do so while accounting for their limitations, which are themselves only poorly understood? We develop an econometric framework to answer this question that distinguishes between two types of...

A common practice in evidence-based decision-making uses estimates of conditional probabilities P(y|x) obtained from research studies to predict outcomes y on the basis of observed covariates x. Given this information, decisions are then based on the predicted outcomes. Researchers commonly...

We partner with a large US payment-processing company to run a 5-year, 10 wave panel survey of incentivized quarterly sales forecasts on over 6,000 firms. We match firm predictions to proprietary revenue data to assess accuracy. We find firms forecast poorly, with issues of inaccuracy,...

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 assemble the most comprehensive dataset to date on the characteristics of colleges and universities, including dates of operation, institutional setting, student body, staff, and finance data from 2002 to 2023. We provide an extensive description of what is known and unknown...

When race is not directly observed, regulators and analysts commonly predict it using algorithms based on last name and address. In small business lending--where regulators assess fair lending law compliance using the Bayesian Improved Surname Geocoding (BISG) algorithm--we document large...

Forecasts of the rate of price inflation play a central role in the formulation of monetary policy, and forecasting inflation is a key job for economists at the Federal Reserve Board. This paper examines whether this job has become harder and, to the extent that it has, what changes in the...

This article examines the performance of various financial variables as predictors of subsequent U.S. recessions. Series such as interest rates and spreads, stock prices, currencies, and monetary aggregates are evaluated singly and in comparison with other financial and non-financial indicators....