We derive asymptotic properties of estimators and test statistics to determine - in a grouped data setting - common versus group-specific factors. Despite the fact that our test statistic for the number of common factors, under the null, involves a parameter at the boundary (related to unit...

We propose a method to correct estimates from historical linked data for bias arising from type-I error--"false matches." We estimate the rate of false matching from the disagreement rate in characteristics that should agree across the two linked datasets. Combined with an understanding of the...

This chapter provides an overview of and user's guide to dynamic factor models (DFMs), their estimation, and their uses in empirical macroeconomics. It also surveys recent developments in methods for identifying and estimating SVARs, an area that has seen important developments over the past 15...

We study how preferences over the demographic composition of co-patrons affects income segregation in shared spaces. To distinguish demographic preferences from tastes for other venue attributes, we study venue choices within business chains. We find two notable regularities: preferences for...

A common tactic to estimate willingness-to-travel exploits variation in the relative proximity of consumers to supplier locations. The validity of these estimates relies on the exogeneity of that consumer-supplier distance. We argue that distance to suppliers is endogenous because suppliers...

Machine learning methods in asset pricing are often criticized for their black box nature. We study this issue by predicting corporate bond returns using interpretable machine learning on a high-dimensional bond characteristics dataset. We achieve state-of-the-art performance while maintaining...

New data sources, commonly referred to as “Big Data”, have attracted growing interest from National Statistical Institutes. They have the potential to complement official and more conventional statistics used, for instance, to determine progress towards the Sustainable Development Goals...

We study positional portfolio management strategies in which the manager maximizes an expected utility function written on the cross-sectional rank (position) of the portfolio return. The objective function reflects the manager's goal to be well-ranked among competitors. To implement positional...

We use machine learning algorithms to construct a novel news-based index of US environmental and climate policy uncertainty (EnvPU) available on a monthly basis over the 1990-2019 period. We find that our EnvPU index spikes during the environmental spending disputes of the 1995-1996 government...

We review, from a practical standpoint, the evolving literature on assessing external validity (EV) of estimated treatment effects. We provide an implementation and real-world assessment of the general EV measures developed in Bo and Galiani (2021). In the context of estimating conditional...