Dynamics and Uncertainty in Macroeconomics

在过去的几年里，宏观经济学在结合现实和经验相关的动态来源，以及在不确定性下的决策处理方面取得了进展。拟议的研究包括两个不同但相关的项目，探讨经济动态和不确定性的影响。 第一个项目开发新的经验方法来指定和测量与经济模型相关的不确定性，并研究不确定性对决策的影响。 第二个项目开发和应用方法来研究自适应学习作为经济模型中放大或传播的来源的定量重要性。 不确定性在经济学中无处不在，经济行为人和政策制定者必须不断面对这种不确定性。 第一个项目展示了如何构建和测量与估计模型相关的不确定性;开发明确考虑模型不确定性的估计方法;提供最小化这种不确定性的估计;并使用这些结果来设计对我们估计的不确定性具有鲁棒性的决策规则。 该项目提供了一个新的，连贯的框架来处理不确定性，允许在不确定的环境中的替代决策规则的性能进行认真的实证评估。 这些方法被应用于货币政策规则的设计和金融风险的对冲。 虽然商业周期波动是宏观经济学的核心问题，但许多领先的商业周期模型难以匹配观测数据的持续性和波动性。虽然现代宏观经济学的大部分是基于理性预期理论，但越来越多的文献研究了那些随着时间的推移而学习的不完全理性的经济体。第二个项目说明学习如何在经济冲击传播过程中提供额外的持久性，以及在某些情况下学习动态如何成为经济波动的新来源。 该项目量化了这些学习动态在解释和产生商业周期波动方面的重要性。该研究的许多结果具有很强的政策含义，例如，如果学习是经济波动的一个整体特征，那么改善信息传递的政策可能是减少波动的关键。更直接地说，第一个项目的一个特别重点是衡量和处理货币政策的不确定性。通过分析不确定性的经验相关来源，本研究提供了帮助，在设计政策规则，将导致良好的经济表现。

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.