Nowcasting GDP with a pool of factor models and a fast estimation algorithm

Sercan Eraslan and Maximilian Schröder

International Journal of Forecasting

PDF Published Version PDF Bundesbank Discussion Paper 41/2019

Abstract

We propose a novel mixed-frequency dynamic factor model with time-varying parameters and stochastic volatility for macroeconomic nowcasting and develop a fast estimation algorithm. This enables us to generate forecast densities based on a large space of factor models. We apply our framework to nowcast US GDP growth in real time. Our results reveal that stochastic volatility seems to improve the accuracy of point forecasts the most, compared to the constant-parameter factor model. These gains are most prominent during unstable periods such as the Covid-19 pandemic. Finally, we highlight indicators driving the US GDP growth forecasts and associated downside risks in real time.