Learning and the Planning Horizon: Applications to Economic Fluctuations, Asset Prices and Policy

学习和规划视野：在经济波动、资产价格和政策中的应用

• 批准号: 1025011
• 负责人: George Evans
• 金额: $ 17.02万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025011&HistoricalAwards=false
• 关键词: Learning; Planning; Horizon; Applications; Economic

LEARNING AND THE PLANNING HORIZON: APPLICATIONS TO ECONOMIC FLUCTUATIONS, ASSET PRICES AND POLICYIntellectual Merit. A central feature of modern economic theory is forward-looking decision making by firms and households. While the rational expectations approach provides the benchmark theory of expectation formation, adding learning dynamics has yielded many novel insights. The proposed research will advance this research conceptually and examine several prominent issues in macroeconomics. Theoretical issues will focus on incorporation of structural knowledge, implications of the planning horizon, and eductive stability in infinite-horizon models. Applications will include monetary and fiscal policy in deep recessions, the impact of anticipated future changes in fiscal policy, and the tendency for asset prices to exhibit bubbles and crashes. Unifying themes are that learning impacts both the stability of the system and the propagation mechanisms for shocks. The research will focus on five interconnected lines of research:(i) Macroeconomic policy in New Keynesian models. Even when private agents use long-horizon decision rules, a large pessimistic expectations shock can push the economy along a trajectory of falling output and deflation. This research will study alternative monetary and fiscal policies for avoiding deep recession and deflation, and which return the economy to equilibrium. The implications of heterogeneous expectations for monetary policy will also be studied.(ii) Anticipated policy and learning. This research will show how private agents can combine econometric learning with structural knowledge about future policy changes. Examples include the impact on hours and investment of anticipated future changes in government spending and taxes. The scope of validity of Ricardian equivalence under learning will receive particular attention.(iii) Learning to optimize and the planning horizon. This project proposes a natural model of bounded optimality for long-horizon agents. The research will show how solving and updating suitable two-period decision problems, using adaptive learning rules, can converge to fully optimal decisions. The methodology will be extended to cover real business cycle (RBC) type models.(iv) Financial markets. One application will examine the impact of learning about risk and return on stock price dynamics, and determine the conditions in which bubbles and crashes are likely to emerge. A second application will look at the possibility of financial collapse when adaptive learning is introduced into a model of financial intermediation with long-term relationships.(v) Eductive stability in RBC models. This research will examine whether mental reasoning based on common knowledge can lead agents to coordinate on rational expectations in RBC models. Eductive learning combined with long-horizon decision-making appears prone to cyclical dynamics and even instability. Methods for combining eductive and adaptive learning will also be explored.Broader Impacts. The broader aim of the project is to inform policymakers of the need to take into account learning and bounded rationality by private agents and policymakers themselves. The importance of learning, expectations and model uncertainty, for monetary and fiscal policy, is increasingly being recognized by policymakers. In the last five years the PI has been a visiting scholar at the Cleveland and St. Louis Federal Reserve Banks and made presentations at the Board of Governors, the Kansas City and San Francisco FRBs, the Bank of Japan, the Bank of England, the Banque de France, the Central Bank of Chile, the ECB, the IMF and the IMF Institute. The PI has also co-authored surveys, including one aimed at policymakers. The research from the proposed project will be disseminated widely in seminars, workshops and conferences, at universities and central banks. Research papers from the project will be made available on the web. The project will also support the research of graduate students. Past NSF grants by the PI have supported research on related topics and led to PhD theses and academic appointments at research universities.

学习与规划视野：经济波动、资产价格与政策的应用。现代经济理论的一个核心特征是企业和家庭的前瞻性决策。虽然理性预期方法提供了预期形成的基准理论，但增加学习动态产生了许多新的见解。拟议的研究将在概念上推进这一研究，并探讨宏观经济学中的几个突出问题。理论问题将集中在纳入结构知识，规划视野的影响，和教育的稳定性在无限视野模型。应用将包括深度衰退中的货币和财政政策，财政政策预期未来变化的影响，以及资产价格出现泡沫和崩溃的趋势。统一的主题是，学习影响系统的稳定性和冲击的传播机制。研究将集中在五个相互关联的研究领域：（一）新凯恩斯主义模型中的宏观经济政策。即使私人代理人使用长期决策规则，巨大的悲观预期冲击也可能推动经济沿着产出下降和通货紧缩的轨道前进。本研究将研究避免深度衰退和通货紧缩的替代货币和财政政策，并使经济恢复平衡。我们还将研究异质预期对货币政策的影响。(ii)预期的政策和学习。这项研究将展示私人代理人如何将联合收割机计量经济学学习与未来政策变化的结构知识结合起来。例如，政府支出和税收的预期未来变化对工作时间和投资的影响。在学习条件下，Riccott等值的有效性范围将受到特别的关注。(iii)学习优化和规划视野。该项目提出了一个自然模型的有界最优的长期视野代理。该研究将展示如何解决和更新合适的两阶段决策问题，使用自适应学习规则，可以收敛到完全最优的决策。该方法将扩展到涵盖真实的商业周期（RBC）类型模型。(iv)金融市场其中一个应用程序将研究学习风险和回报对股票价格动态的影响，并确定泡沫和崩溃可能出现的条件。第二个应用程序将研究当自适应学习引入具有长期关系的金融中介模型时，金融崩溃的可能性。(v)RBC模型的教育稳定性。本研究将探讨基于常识的心理推理是否可以导致智能体在RBC模型中协调理性预期。教育性学习与长期决策相结合似乎容易出现周期性动态，甚至不稳定。还将探索结合教育性学习和适应性学习的方法。更广泛的影响。该项目更广泛的目标是让决策者认识到需要考虑到私人代理人和决策者本身的学习和有限理性。决策者日益认识到学习、预期和模型不确定性对货币和财政政策的重要性。在过去的五年里，PI一直是克利夫兰和圣路易斯联邦储备银行的访问学者，并在理事会，堪萨斯城和旧金山联邦储备银行，日本银行，英格兰银行，法兰西银行，智利中央银行，欧洲央行，国际货币基金组织和国际货币基金组织研究所发表演讲。PI还共同撰写了调查，包括一项针对政策制定者的调查。拟议项目的研究成果将在大学和中央银行的研讨会、讲习班和会议上广泛传播。该项目的研究论文将在网上提供。该项目还将支持研究生的研究。PI过去的NSF赠款支持了相关主题的研究，并导致了研究型大学的博士论文和学术任命。