Nowcasting and short-term forecasting of G-20 countries GDP with endogenous regime-switching MIDAS models

The paper investigates the application of endogenous Markov-switching MIDAS models for nowcasting and forecasting during the turbulent economic landscape shaped by the COVID-19 crisis. I extend the standard MS MIDAS model to incorporate time-varying transition probabilities whose dynamics is influenced by various explanatory variables, enabling a more nuanced understanding of GDP dynamics across G-20 economies. The analysis reveals that these models enhance forecasting accuracy compared to standard MIDAS models, particularly in crisis periods, by better capturing the timing and magnitude of economic shifts. I point out the importance of selecting appropriate indicators at varying forecasting horizons, as their effectiveness fluctuates among forecasting horizons. My findings underscore the potential of Markov-switching models, particularly those with endogenous switching, as promising tools for macroeconomic forecasting, advocating for their further development across multiple modeling frameworks.