Two often-divergent U.S. GDP estimates are available, a widely-used expenditure-side version GDPE, and a much less widely-used income-side version GDI . The authors propose and explore a "forecast combination" approach to combining them. They then put the theory to work, producing a superior...

The authors propose several connectedness measures built from pieces of variance decompositions, and they argue that they provide natural and insightful measures of connectedness among financial asset returns and volatilities. The authors also show that variance decompositions define weighted,...

The authors sketch a framework for monitoring macroeconomic activity in real-time and push it in new directions. In particular, they focus not only on real activity, which has received most attention to date, but also on inflation and its interaction with real activity. As for the recent...

Prediction problems involving asymmetric loss functions arise routinely in many fields, yet the theory of optimal prediction under asymmetric loss is not well developed. We study the optimal prediction problem under general loss structures and characterize the optimal predictor. We compute it...

We provide a new and superior measure of U.S. GDP, obtained by applying optimal signal-extraction techniques to the (noisy) expenditure-side and income-side estimates. Its properties -- particularly as regards serial correlation -- differ markedly from those of the standard expenditure-side...

The authors propose a constructive, multivariate framework for assessing agreement between (generally misspecified) dynamic equilibrium models and data, which enables a complete second-order comparison of the dynamic properties of models and data. They use bootstrap algorithms to evaluate the...