This thesis focuses on uncertainty which begins with Knightian Uncertainty. First we introduce a concept of time-varying uncertainty aversion. We find that uncertainty aversion is increasing before the crash and is resolved after the crash, and tends to move together with S&P 500. Second we present a relationship between uncertainty aversion and business condition. We construct a VECM regression and Granger Causality tests. Using credit spread and term spread as indicators of business conditions, we find some interesting results: (1) Uncertainty aversion has significant positive relationship with credit spreads in United States. (2) Uncertainty aversion has no significant relationship with term-spreads. (3) Uncertainty aversion granger causes both credit spreads and term spreads. This implies that with uncertainty aversion we can explain the credit spread puzzle as well as we can predict future business conditions. If today’s uncertainty increases, tomorrow’s business condition will be worse, and if today’s uncertainty decreases or is resolved, tomorrow’s business condition will be better.
Keywords: Model Uncertainty; Robust Control; Bayesian Learning; Uncertainty Aversion; Business Conditions;