Forecasting GDP over the business cycle in a multi-frequency and data-rich environment

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