A Factor-Augmented Probit Model For Business Cycle Analysis
Dimension reduction of large data sets has been recently the topic of interest of many research papers dealing with macroeconomic modeling. Especially dynamic factor models have been proved to be useful for GDP now casting or short-term forecasting. In this paper, the authors put forward an innovative factor-augmented probity model in order to analyze the business cycle. Factor estimation is carried either by standard statistical methods or by allowing a richer dynamic behavior. An application is provided on euro area data in order to point out the ability of the model to detect recessions over the period 1974-2008.