Expectation Coordination and Agent-level Learning

期望协调和代理级学习

• 批准号: 1559209
• 负责人: George Evans
• 金额: $ 31.76万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1559209&HistoricalAwards=false
• 关键词: Expectation; Coordination; Agent; level; Learning

The PIs propose work that will use agent- level modeling methods to address research questions in macroeconomics. The PIs want to examine the behavior of a computational model of an entire economy. In this model, firms and consumers are boundedly rational and use adaptive learning rules. The PIs plan to embed this framework into a dynamic stochastic general equilibrium model and to allow for heterogeneous agents; so-called DSGE models are widely used in macroeconomics, and the PIs will use both Real Business Cycle and New Keynesian models. These DSGE models often have multiple possible outcomes that can be supported by rational behavior on the part of individuals via self-fulfilling prophecies. Economists call these "sunspot" equilibria; if everyone in the economy thinks that a signal, perhaps in the form of some external event (even an astronomical event that has no effect on the planet), predicts an upcoming recession, then after seeing the signal, people may start spending less money in anticipation of possible layoffs, and employers may begin to lay off workers in anticipation of a drop in sales. The result is a recession, even though the signal itself has no direct effect on any individual in the economy. The PIs will examine when sunspot equilibria are stable over time when people learn from experience, thus formalizing the process by which fear of recession leads to recession Modeling people as computational agents with a simple adaptive learning rule is one way to examine the stability of sunspot equilibria. They will also examine the effect of government policies such as monetary and fiscal policy in models with this kind of adaptive learning. The goal here is to examine agent-level learning in general equilibrium environments. Recent work by the research team has demonstrated that boundedly rational agents can learn to solve dynamic optimization problems over time by in effect replacing them with repeated two-period problems, while updating in each period their forecasts of shadow prices of key state variables. The project will embed this and related implementations of learning into DSGE models. The agent-level approach facilitates solving computational models with heterogeneous firms and households; this allows both for the study of macroeconomic policy and for the examination of wealth inequality. The PIs will re-examine the learning stability of sunspot equilibria in Real Business Cycle and New Keynesian models. They will also examine models in which the zero-lower-bound on interest rates leads to multiple steady states. And they will examine the impact of fiscal policy and government spending multipliers in New Keynesian models with learning. The proposed research will have specific implications for both monetary and fiscal policy, in addition to demonstrating how the PI?s bounded optimality approach can be used in a wide range of applied macroeconomic models.

PI提出的工作将使用代理级建模方法来解决宏观经济学中的研究问题。PI想要检查整个经济的计算模型的行为。在这个模型中，企业和消费者是有限理性的，并使用自适应学习规则。PI计划将这一框架嵌入到动态随机一般均衡模型中，并考虑到异质代理;所谓的DSGE模型广泛用于宏观经济学，PI将使用真实的商业周期和新凯恩斯主义模型。这些DSGE模型通常有多种可能的结果，可以通过自我实现的预言得到个体理性行为的支持。经济学家称之为“太阳黑子”均衡;如果经济体中的每个人都认为一个信号，也许是某种外部事件（甚至是对地球没有影响的天文事件）的形式，预示着即将到来的衰退，那么在看到这个信号后，人们可能会开始减少支出，因为预期可能会裁员，雇主可能会开始裁员，因为预期销售额会下降。其结果是经济衰退，尽管信号本身对经济中的任何个人都没有直接影响。当人们从经验中学习时，PI将检查太阳黑子平衡何时随着时间的推移而稳定，从而将衰退恐惧导致衰退的过程形式化。他们还将研究政府政策（如货币和财政政策）在具有这种自适应学习的模型中的影响。这里的目标是研究一般均衡环境中的代理级学习。研究小组最近的工作表明，有限理性的代理人可以学习解决动态优化问题，随着时间的推移，实际上是用重复的两期问题代替它们，同时在每个时期更新他们对关键状态变量的影子价格的预测。该项目将把这一点和相关的学习实现嵌入DSGE模型。代理人层面的方法有助于解决计算模型与异质企业和家庭，这使得宏观经济政策的研究和财富不平等的检查。本研究将重新检验太阳黑子均衡在真实的商业周期和新凯恩斯模型中的学习稳定性。他们还将研究利率零下限导致多个稳态的模型。他们将研究财政政策和政府支出乘数在新凯恩斯主义模型中的影响。拟议的研究将有具体的影响，货币和财政政策，除了演示如何PI？的有界最优方法可以用于广泛的应用宏观经济模型。