Dynamics and Uncertainty in Macroeconomics

宏观经济学中的动态和不确定性

• 批准号: 0317848
• 负责人: Noah Williams
• 金额: $ 14.8万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0317848&HistoricalAwards=false
• 关键词: Dynamics; Uncertainty; Macroeconomics

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.

在过去几年中，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处理决策方面取得了进展。拟议的研究包括两个不同但相关的项目，探索经济动态和不确定性影响的各个方面。第一个项目开发新的实证方法来具体说明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。第二个项目开发并应用方法来研究适应性学习在经济模型中作为放大或传播来源的定量重要性。不确定性在经济学中是普遍存在的，代理人和决策者必须不断地面对这种不确定性。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；提供使这种不确定性最小化的估计；并利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。该项目为处理不确定性提供了一个新的、连贯的框架，允许在不确定环境中对备选决策规则的性能进行认真的经验评估。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一个核心问题，但许多主要的商业周期模型难以与观察到的数据的持久性和波动性相匹配。虽然现代宏观经济学的大部分内容都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主体的经济。第二个项目显示了学习如何能够在经济冲击的传播中提供额外的持久性，以及在某些情况下学习动态如何能够提供经济波动的新来源。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。更广泛的影响。提案中的许多结果具有强烈的政策含义。例如，如果学习是经济波动的一个整体特征，那么能够改善信息传递的政策可能对减少波动至关重要。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。通过分析实证相关的不确定性来源，本研究有助于设计政策规则，从而实现良好的经济绩效。