Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Jena Economic Research Papers ; No. 2019-006

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods ; Business Fluctuations; Cycles ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
• Subject: Markov-Switching Dynamic Factor Model ; Great Recession ; Turning Points ; GDP Nowcasting ; GDP Forecasting

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Carstensen, Kai ; Heinrich, Markus ; Reif, Magnus ; Wolters, Maik H.
• Event: Veröffentlichung
• (who): Friedrich Schiller University Jena
• (where): Jena
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/204610
• Last update: 10.03.2025, 11:43 AM CET