Forecasting US Recessions with a Large Set of Predictors.

In this paper, I use a large set of macroeconomic and financial predictors to forecast US recession periods. I adopt Bayesian methodology with shrinkage in the parameters of the probit model for the binary time series tracking the state of the economy. The in-sample and out-of-sample results show that utilizing a large cross-section of indicators yields superior US recession forecasts in comparison to a number of parsimonious benchmark models. Moreover, the datarich probit model gives similar accuracy to the factor-based model for the 1-month-ahead forecasts, while it provides superior performance for 1-year-ahead predictions. Finally, in a pseudo-real-time application for the Great Recession, I find that the large probit model with shrinkage is able to pick up the recession signals in a timely fashion and does well in comparison to the more parsimonious specification and to nonparametric alternatives. [ABSTRACT FROM AUTHOR]