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

Marie Bessec (1) , Othman Bouabdallah (2)

1 LEDA-CGEMP - Centre de Géopolitique de l’Energie et des Matières Premières
2 European Central Bank

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.

Mots clés: Markov-Switching; factor models; mixed frequency data; GDP forecasting

Domaines: Économie et finance quantitative [q-fin]

Marie Bessec, Othman Bouabdallah. Forecasting GDP over the business cycle in a multi-frequency and data-rich environment. Oxford Bulletin of Economics and Statistics, 2015, 77 (3), ⟨10.1111/obes.12069⟩. ⟨hal-01275760⟩