An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models

• Activity Number: 270
• Type: Invited
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Scott Holan*+ and Wen-Hsi Yang and David Scott Matteson and Christopher K. Wikle
• Companies: University of Missouri and CSIRO Computational Informatics and Cornell University and University of Missouri
• Keywords: Bayesian model averaging ; Business cycles ; Empirical orthogonal functions ; Functional data ; Spectrogram ; Stochastic search variable selection

Abstract

Identifying periods of recession and expansion is a topic of ongoing interest with important economic and monetary policy implications. Given the current state of the global economy, significant attention has recently been devoted to identifying and forecasting economic recessions. Consequently, we introduce a novel class of Bayesian hierarchical probit models that take advantage of dimension-reduced time-frequency representations of various market indices. The approach we propose relates high-frequency daily data observed over several quarters to a binary quarterly response indicating recession or expansion. Specifically, our model directly incorporates time-frequency representations of the entire high-dimensional nonstationary time series of daily log returns, over several quarters, as a regressor in a predictive model, while quantifying various sources of uncertainty. The effectiveness of our model is demonstrated through out-of-sample identification (nowcasting) and multistep-ahead prediction of economic recessions. Our results provide greater than 85% and 80% out-of-sample forecasting accuracy for recessions and expansions respectively, even three quarters ahead.