In a factor-augmented regression, the forecast of a variable depends on a few factors estimated from a large number of predictors. But how does one determine the appropriate number of factors relevant for such a regression? Existing work has focused on criteria that can consistently estimate the...

In this paper, we seek to produce forecasts of commodity price movements that can systematically improve on naïve statistical benchmarks. We revisit how well changes in commodity currencies perform as potential efficient predictors of commodity prices, a view emphasized in the recent...

We compare a number of data-rich prediction methods that are widely used in macroeconomic forecasting with a lesser known alternative: partial least squares (PLS) regression. In this method, linear, orthogonal combinations of a large number of predictor variables are constructed such that the...

We suggest a way to perform parsimonious instrumental variables estimation in the presence of many, and potentially weak, instruments. In contrast to standard methods, our approach yields consistent estimates when the set of instrumental variables complies with a factor structure. In this sense,...

This paper revisits inflation forecasting using reduced-form Phillips curve forecasts, that is, inflation forecasts that use activity and expectations variables. We propose a Phillips-curve-type model that results from averaging across different regression specifications selected from a set of...

Theories of systemic risk suggest that financial intermediaries' balance-sheet constraints amplify fundamental shocks. We provide supportive evidence for such theories by decomposing the U.S. dollar risk premium into components associated with macroeconomic fundamentals and a component...