A0518
Title: An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors
Authors: Yang Liu - Rutgers University (United States) [presenting]
Abstract: 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, are evaluated. The predictors are defined as selected observable variables and latent factors extracted from a high dimensional macroeconomic dataset, and the EUIs are functions of predictive errors from models that incorporate these predictors. The estimation of the predictors is based on a number of extant and novel machine-learning methods that combine dimension reduction and shrinkage. When predicting 14 monthly U.S. economic series selected from 8 different groups of economic variables, the new indexes and predictors are shown to result in significant improvements in forecast accuracy relative to predictions made using benchmark models. Moreover, while the inclusion of either BC predictors or EUIs often yields forecast accuracy improvements, greater predictive gains accrue when using BC predictors with real economic activity type variables. Also, adding both BC predictors and EUIs together is particularly useful when forecasting housing market variables at short horizons.
