A Neural Network Ensemble Approach for GDP Forecasting

Abstract

We propose an ensemble learning methodology to forecast the future US GDP growth release. Our approach combines a Recurrent Neural Network (RNN) with a Dynamic Factor model accounting for time-variation in the mean with a Generalized Autoregressive Score (DFM-GAS). We show how this combination improves forecasts in the aftermath of the 2008-09 global financial crisis, as well as in the latest COVID-19 recession, by reducing the root mean squared error for the short-term forecast horizon. Thus, we evaluate the predictive ability of each component of the ensemble by considering variations in the mean, as the latter are potentially caused by recessions affecting the economy. For our scope, we employ a set of predictors encompassing a wide range of variables measured at different time frequencies. Thus, we provide dynamic coefficients for predictors after an interpretable machine learning routine to assess how the model reflects the evolution of the business cycle.