The Impact of Macroeconomic and Financial Uncertainty on GDP Growth: a Quantile Regression Analysis

This dissertation investigates the relationship between financial and macroeconomic uncertainty and the business cycle. Utilizing quantile regression analysis and US data, the study examines the effects of changes in uncertainty on the entire conditional distribution of future real GDP growth over different time horizons. Key findings reveal that financial uncertainty predominantly signals downside risk, while macroeconomic uncertainty enhances both risks and growth opportunities. The analysis underscores the importance of conditioning on different phases of the business cycle, as different crises episodes show different impacts of uncertainty measures. Use of vulnerability measures such as relative entropy and expected shortfall highlight asymmetries in GDP growth risks. Addressing reverse causality, the research finds limited reverse impact of output growth on uncertainty. The results emphasize the need for precise identification of uncertainty channels affecting economic outcomes. This work contributes to understanding the distinct roles of financial and macroeconomic uncertainty, suggesting that policymakers should use detailed uncertainty measures and recognize non-linear transmission channels. Future research should refine uncertainty specifications to better capture its effects, to help achieve more effective policies and improve crisis management.