Arbeitspapier
Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Jena Economic Research Papers ; No. 2019-006
- Klassifikation
-
Wirtschaft
Forecasting Models; Simulation Methods
Business Fluctuations; Cycles
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- Thema
-
Markov-Switching Dynamic Factor Model
Great Recession
Turning Points
GDP Nowcasting
GDP Forecasting
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Carstensen, Kai
Heinrich, Markus
Reif, Magnus
Wolters, Maik H.
- Ereignis
-
Veröffentlichung
- (wer)
-
Friedrich Schiller University Jena
- (wo)
-
Jena
- (wann)
-
2019
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Carstensen, Kai
- Heinrich, Markus
- Reif, Magnus
- Wolters, Maik H.
- Friedrich Schiller University Jena
Entstanden
- 2019
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