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Measuring Micro and Macro Uncertainty.

Liu, Yang.

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Measuring Micro and Macro Uncertainty.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Measuring Micro and Macro Uncertainty./
Author:	Liu, Yang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	114 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-10, Section: A.
Contained By:	Dissertations Abstracts International80-10A.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13810300
ISBN:	9781392063224

Measuring Micro and Macro Uncertainty.
Liu, Yang.

Measuring Micro and Macro Uncertainty. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 114 p.

Source: Dissertations Abstracts International, Volume: 80-10, Section: A.

Thesis (Ph.D.)--American University, 2019.

This item must not be sold to any third party vendors.

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 macroeconomic uncertainty that incorporates rich information set from U.S. SPF density forecasts. The measure has two key advantages over traditional measures: (i) it reflects the subjective perceptions of market participants; (ii) it is an ex-ante measure that does not require the knowledge of realized outcomes. I study the features of this measure of macroeconomic uncertainty and explore its impact on real economic activities within the U.S. as well as its spillover effects on BRIC countries. The Appendix discusses the methodology used in Chapter 3.

ISBN: 9781392063224Subjects--Topical Terms:

517247
Statistics.

Measuring Micro and Macro Uncertainty.
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520 $a 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 macroeconomic uncertainty that incorporates rich information set from U.S. SPF density forecasts. The measure has two key advantages over traditional measures: (i) it reflects the subjective perceptions of market participants; (ii) it is an ex-ante measure that does not require the knowledge of realized outcomes. I study the features of this measure of macroeconomic uncertainty and explore its impact on real economic activities within the U.S. as well as its spillover effects on BRIC countries. The Appendix discusses the methodology used in Chapter 3.
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