Expectation Coordination and Agent-level Learning

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。在该模型中，企业和消费者是有限理性的，使用自适应学习规则。pi计划将该框架嵌入到动态随机一般均衡模型中，并允许异质代理；所谓的DSGE模型在宏观经济学中被广泛使用，pi将同时使用真实商业周期和新凯恩斯模型。这些DSGE模型通常有多种可能的结果，这些结果可以通过个体的理性行为通过自我实现的预言来支持。经济学家称之为“太阳黑子”均衡；如果经济中的每个人都认为一个信号，也许是一些外部事件（甚至是一个天文事件，对地球没有影响）的形式，预示着即将到来的经济衰退，那么在看到这个信号之后，人们可能会开始减少消费，因为预计可能会裁员，雇主可能会开始裁员，因为预计销售额会下降。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稳定，从而形式化对衰退的恐惧导致衰退的过程，将人建模为具有简单适应性学习规则的计算代理是检查太阳黑子平衡稳定性的一种方法。他们还将在具有这种适应性学习的模型中检验政府政策（如货币和财政政策）的效果。这里的目标是研究一般均衡环境下的主体级学习。研究小组最近的工作表明，有界理性的代理人可以学会解决动态优化问题，方法是用重复的两期问题代替动态优化问题，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。该项目将把这个和相关的学习实现嵌入到DSGE模型中。代理级方法有助于求解具有异质企业和家庭的计算模型；这既可以研究宏观经济政策，也可以研究财富不平等。pi将在真实商业周期和新凯恩斯模型中重新检验太阳黑子均衡的学习稳定性。他们还将研究利率下限为零导致多个稳定状态的模型。他们将通过学习来研究财政政策和政府支出乘数在新凯恩斯模型中的影响。拟议的研究将对货币和财政政策产生具体的影响，除了证明PI如何？S有界最优性方法可用于广泛的应用宏观经济模型。

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.