Nowcasting GDP with a large factor model space - Sercan Eraslan; Maximilian Schröder (Deutsche Bundesbank, 2019)
We propose a novel time-varying parameters mixed-frequency dynamic factor model which is integrated into a dynamic model averaging framework for macroeconomic nowcasting. Our suggested model can efficiently deal with the nature of the real-time data flow as well as parameter uncertainty and time-varying volatility. In addition, we develop a fast estimation algorithm. This enables us to generate nowcasts based on a large factor model space. We apply the suggested framework to nowcast German GDP. Our recursive out-of-sample forecast evaluation results reveal that our framework is able to generate forecasts superior to those obtained from a naive and more competitive benchmark models. These forecast gains seem to emerge especially during unstable periods, such as the Great Recession, but also remain over more tranquil periods.
Keywords: C11; C32; C51; C52; C53; 330; dynamic factor model; forecasting; GDP; mixed-frequency; model averaging; time-varying-parameter