Federal statistical agencies in the United States and analogous agencies elsewhere commonly report official economic statistics as point estimates, without accompanying measures of error. Users of the statistics may incorrectly view them as error-free or may incorrectly conjecture error...

This study examines the impact of access to confidential administrative data on the rate, direction, and policy relevance of economics research. To study this question, we exploit the progressive geographic expansion of the U.S. Census Bureau's Federal Statistical Research Data Centers (FSRDCs)....

We provide a framework for evaluating and improving multivariate density forecasts. Among other things, the multivariate framework lets us evaluate the adequacy of density forecasts involving cross-variable interactions, such as time-varying conditional correlations. We also provide conditions...

We argue that comprehensive out-of-sample (OOS) evaluation using statistical decision theory (SDT) should replace the current practice of K-fold and Common Task Framework validation in machine learning (ML) research. SDT provides a formal framework for performing comprehensive OOS evaluation...

This paper uses Wald's concept of the risk of a statistical decision function to address the question: How should sample data on treatment response be used to guide treatment choices in a heterogeneous population? Statistical treatment rules (STRs) are statistical decision functions that map...

Since 1979, the Bureau of Labor Statistics (BLS) has nearly quadrupled the size of the sample used to estimate monthly employment changes. Although first-reported employment estimates are still noisy, the magnitude of sampling variability has declined in proportion to the increase in the sample...