Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model - Kai Carstensen; Markus Heinrich; Magnus Reif; Maik H. Wolters (Friedrich Schiller University Jena, 2019)
We estimate a Markow-switching dynamic factor model with three states based on six leading business cycle indicators for Germany preselected from a broader set using the Elastic Net soft-thresholding rule. The three states represent expansions, normal recessions and severe recessions. We show that a two-state model is not sensitive enough to reliably detect relatively mild recessions when the Great Recession of 2008/2009 is included in the sample. Adding a third state helps to clearly distinguish normal and severe recessions, so that the model identifies reliably all business cycle turning points in our sample. In a real-time exercise the model detects recessions timely. Combining the estimated factor and the recession probabilities with a simple GDP forecasting model yields an accurate nowcast for the steepest decline in GDP in 2009Q1 and a correct prediction of the timing of the Great Recession and its recovery one quarter in advance.
Keywords: C53; E32; E37; 330; Markov-Switching Dynamic Factor Model; Great Recession; Turning Points; GDP Nowcasting; GDP Forecasting