Nowcasting GDP with a pool of factor models and a fast estimation algorithm
提出一种含时变参数和随机波动率的混频动态因子模型，配合快速估计算法，基于大量因子模型生成预测密度，用于实时预测美国GDP增长，发现随机波动率在疫情等不稳定时期显著提升点预测精度。
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.