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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

International Journal of Forecasting, 2020, vol. 36, issue 3, 829-850

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.

Keywords: Markov-switching dynamic factor model; Great Recession; Turning points; GDP nowcasting; GDP forecasting (search for similar items in EconPapers)
Date: 2020
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Related works:
Working Paper: Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model (2019) 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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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:3:p:829-850

DOI: 10.1016/j.ijforecast.2019.09.005

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