Dynamics and Uncertainty in Macroeconomics

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In the past few years, macroeconomics has advanced in incorporating realistic and empirically relevant sources of dynamics, and in the treatment of decision making under uncertainty. The proposed research consists of two distinct but related projects that explore aspects of economic dynamics and the effects of uncertainty. The first project develops new empirical methods to specify and measure uncertainty associated with economic models, and studies the effects of uncertainty on decisions. The second project develops and applies methods to study the quantitative importance of adaptive learning as a source of amplification or propagation in economic models. Uncertainty is pervasive in economics, and this uncertainty must be faced continually by agents and policy makers. The first project shows how to structure and measure the uncertainty associated with an estimated model; develops estimation methods that explicitly account for model uncertainty; provides estimates which minimize a measure of this uncertainty; and uses these results to design decision rules which are robust to the uncertainty that we estimate. This project provides a new, coherent framework for dealing with uncertainty, allowing for a serious empirical evaluation of the performance of alternative decision rules in an uncertain environment. These methods are applied to the design of monetary policy rules, and the hedging of financial risks. Although business cycle fluctuations are a central issue in macroeconomics, many leading business cycle models have difficulty matching the persistence and volatility of observed data. While much of modern macroeconomics is based on the theory of rational expectations, a growing literature has studied economies with less than fully rational agents who learn over time. The second project shows how learning can provide additional persistence in the propagation of economic shocks, and how in some cases learning dynamics can provide a new source of economic fluctuations. The project quantifies the importance of these learning dynamics in explaining and generating business cycle fluctuations.Broader Impacts. Many of the results in the proposal have strong policy implications.For example, if learning is an integral feature of economic fluctuations, then policies which can improve information transmission may be crucial in reducing fluctuations. More directly, a particular focus of the first project is in measuring and dealing with uncertainty in monetary policy. By analyzing the empirically relevant sources of uncertainty, this research provides assistance in designing policy rules which will lead to good economic performance.