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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

No 6457, CESifo Working Paper Series from CESifo Group Munich

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 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: Markov-Switching Dynamic Factor Model; business cycles; Great Recession; leading indicators; turning points; GDP-nowcasting; GDP-forecasting (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-eec, nep-for and nep-mac
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6) Track citations by RSS feed

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