【经管院每周系列讲座第335期】Ambiguity, Low Risk-Free Rates, and Consumption Inequality
主题：Ambiguity, Low Risk-Free Rates, and Consumption Inequality
主讲人：Yulei Luo,The University of Hong Kong
主持人：Wei Wang, AssociateProfessor, RIEM, SWUFE
Dr. Luo is an associate professor of Economics at the University of Hong Kong. He obtained his PhD in Economics from Princeton University. The main research interests of Dr. Luo are macroeconomics, household finance, and international finance. He has several papers on income inequality. His research has been published in peer-reviewed journals including Journal of Economic Theory, Management Science, Journal of the European Economic Association, Journal of International Economics, American Economic Journal: Macroeconomics, Review of Economic Dynamics, Journal of Credit, Money and Banking, Journal of Economic Control and Dynamics, etc.
The failure of macro-economists to predict the Great Recession suggests possible mis-specifications of existing macroeconomic models. If agents bear in mind such possible mis-specifications, how will it alter their optimal decisions and how large are the welfare costs generated by such model uncertainty? To shed light on these questions, we develop a tractable continuous-time recursive utility (RU) version of the Huggett (1993) model to study the effects of model uncertainty due to a preference for robustness (RB, or ambiguity aversion) for the interest rate, the relative dispersion (inequality) of consumption and income, and the welfare costs in general equilibrium. We show that RB, through interacting with inter-temporal substitution and risk aversion, reduces the equilibrium interest rate and the relative dispersion of consumption to income when the income process is stationary, but our benchmark model cannot match the observed relative dispersion. An extension to an RU-RB model with a risky asset is successful at matching this ratio. Our model suggests that a typical consumer in equilibrium would be willing to sacrifice about 12% of his initial income for a 10% reduction in the degree of model uncertainty.