Forecasting GDP over the Business Cycle in a Multi‐Frequency and Data‐Rich Environment
融合MS-MIDAS和因子-MIDAS模型，利用混合频率的大数据集预测GDP增长，并成功识别美国1959-2010年的经济衰退。
Abstract This paper merges two specifications recently developed in the forecasting literature: the MS‐MIDAS model (Guérin and Marcellino, 2013) and the factor‐MIDAS model (Marcellino and Schumacher, 2010). The MS‐factor MIDAS model that we introduce incorporates the information provided by a large data set consisting of mixed frequency variables and captures regime‐switching behaviours. Monte Carlo simulations show that this specification tracks the dynamics of the process and predicts the regime switches successfully, both in‐sample and out‐of‐sample. We apply this model to US data from 1959 to 2010 and properly detect recessions by exploiting the link between GDP growth and higher frequency financial variables.