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

Kai Carstensen, Markus Heinrich, Magnus Reif and Maik Wolters

Munich Reprints in Economics from University of Munich, Department of Economics

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.

Date: 2020
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Citations: View citations in EconPapers (21)

Published in International Journal of Forecasting 3 36(2020): pp. 829-850

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Related works:
Working Paper: Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle (2017)
Working Paper: Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle (2017)
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