| Summary: | This dissertation aims at building alternative measures of economic uncertainty from micro data. The resulting panel measurements enable investigations of real uncertainty effects at the individual, firm, industry, and macro level. Two uncertainty models are introduced to demonstrate additional channels through which uncertainty poses impacts on economic activities. In the first chapter, I propose a novel firm-level uncertainty measure based on the latent conditional volatility of forecast errors. By applying this measure to I/B/E/S database, I track 1,916 U.S. public companies' uncertainties for over 34 years. The firm-level measurements are then aggregated into a macro uncertainty index and the implications at the macro- and micro-level are compared. At the macro level, VAR results indicate a strong "granular origin'' of real uncertainty effects from large firms in addition to the classical short-lived "drop and rebound'' effect. At the firm level, panel regression results confirm the negative impact of macro uncertainty on firm investment and reveal a composite effect of idiosyncratic uncertainty that depends on investment horizon, firm profitability and magnitude of shock (Empirical results are also shown in Chapter 2).The second chapter introduces two uncertainty models that show channels other than "real option'' and "risk aversion & risk premia'' in contemporary literature. The first model is based on Lucas Island Model and Capital Asset Pricing Model (CAPM). It tries to understand the uncertainty effects from the perspective of inefficient expectation and the resulting underproduction problem. The second model inherits New-Keynesian assumptions and features a competition mechanism. It emphasizes the real loss from unexpected supply and demand shocks. Both models include idiosyncratic and macro uncertainty as separate factors and predict a generally negative impact of macro uncertainty versus a composite effect of idiosyncratic uncertainty. In the third chapter, I propose a new measure of ...
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