Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model: An application to the German business cycle

Abstract

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 detect relatively mild recessions reliably when the Great Recession of 2008/2009 is included in the sample. Adding a third state helps to distinguish normal and severe recessions clearly, so that the model identifies all business cycle turning points in our sample reliably. In a real-time exercise, the model detects recessions in a timely manner. 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.