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Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model

Kai Carstensen (), Markus Heinrich (), Magnus Reif and Maik Wolters
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Markus Heinrich: University of Kiel

No 2019-006, Jena Economic Research Papers from Friedrich-Schiller-University Jena

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; Great Recession; Turning Points; GDP Nowcasting; GDP Forecasting (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 (search for similar items in EconPapers)
Date: 2019-09-17
New Economics Papers: this item is included in nep-eec, nep-for, nep-mac and nep-ore
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Related works:
Journal Article: Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model (2020) Downloads
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
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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