An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors

Abstract

In this paper, we evaluate the marginal predictive content of a variety of new business conditions (BC) predictors as well as nine economic uncertainty indexes (EUIs) constructed using these predictors. Our predictors are defined as observable variables and latent factors extracted from a high-dimensional macroeconomic dataset, and our EUIs are functions of predictive errors from models that incorporate these predictors. Estimation of the predictors is based on a number of extant and novel machine learning methods that combine dimension reduction, variable selection, and shrinkage. When predicting 14 monthly U.S. economic series selected from eight different groups of economic variables, our new indexes and predictors are shown to result in significant improvements in forecast accuracy relative to predictions made using benchmark models. In particular, inclusion of either BC predictors or EUIs often yields forecast accuracy improvements, while even greater predictive gains accrue when including both BC predictors and EUIs when forecasting real economic activity-type variables at shorter forecast horizons.