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

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 macroeconomic 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 allowing 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 US business cycle, that the Great Recession and the COVID-19 pandemic only led to temporary increases in volatility, and that US 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 also 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 into recession, the model can generate left-skewed density forecasts without including any financial variable in the information set. The paper finally discusses how to deal with the COVID-19 period when estimating the model.