A0170
Title: Understanding growth-at-risk: A Markov-switching approach
Authors: Francesca Loria - Federal Reserve Board (United States) [presenting]
Abstract: A Markov-switching model is shown with endogenous transition probabilities that can replicate a common finding in the growth-at-risk literature, that is that the (conditional) mean and volatility of future growth are negatively correlated. The model also provides an intuitive interpretation of macroeconomic risk: (endogenous) regime uncertainty generates tail risk. The higher the regime uncertainty, the starker the differences in the growth outlook between a normal and a bad state of the economy. The model is a new tool to assess the risk of tail events, such as recessions, and to evaluate the likelihood of point forecasts. Real-time measures of financial conditions and economic activity are also proposed for the United States and these measures are used to construct conditional quantiles and predictive distributions of average GDP growth over the next 12 months. It is found that periods of high macroeconomic and financial distress, such as the global financial crisis and the COVID-19 pandemic, are associated with low average future growth, high uncertainty, and risks tilted to the downside.
