Switching Macroeconomic Growth and Volatility: Evidence from a Mean-Variance Markov-Switching Dynamic Factor Model

Catherine Doz (), Laurent Ferrara and Pierre-Alain Pionnier
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Catherine Doz: PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris
Pierre-Alain Pionnier: OCDE / OECD - Organisation de Coopération et de Développement Economiques = Organisation for Economic Co-operation and Development

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Abstract: As illustrated by the Great Recession, the COVID-19 pandemic and the global decline in GDP growth since the mid-2000s, economists need to account for sudden and deep recessions, shifts in macroeco- nomic volatility, and longer-term fluctuations in GDP growth. This paper puts forward a Mean-Variance Markov-Switching Dynamic Factor Model (MV-MS-DFM) that accounts for these stylised facts by allow- ing the mean and the volatility of macroeconomic variables to switch abruptly, and trend GDP growth to vary smoothly over time. We show that allowing for different volatility regimes improves the detection of turning points in the U.S. business cycle, that the Great Recession and the COVID-19 pandemic only led to temporary increases in volatility, and that the U.S. trend GDP growth has declined by around 1 percentage point since the early 2000s. Information criteria and marginal likelihood comparisons support our model specification. The model provides a unified framework connecting the literature on turning- point detection to the more recent literature on Growth-at-Risk in macroeconomic forecasting. While tightening financial conditions are shown to increase the probability of falling in a recession, the model can generate left-skewed density forecasts without including any financial variable in the information set. The paper finally discusses how to adjust the model estimation strategy to deal with the COVID-19 period.

Keywords: COVID-19; Growth-at-Risk; Real-time; Businesscycles; Turning-Point Detection; Bayesianestimation; Markov-Switching (search for similar items in EconPapers)
Date: 2025-11
New Economics Papers: this item is included in nep-mac
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Working Paper: Switching Macroeconomic Growth and Volatility: Evidence from a Mean-Variance Markov-Switching Dynamic Factor Model (2025)
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