Collaborative Research - Incomplete Information in Dynamic Economies: Analytics and Applications

自理性预期革命以来，求解动态宏观经济模型中的均衡依赖于施加模型动力学是随机变量期望函数的限制。庇古（1929）认为，经济的结构特征与不完全信息相结合，可能导致悲观和乐观的浪潮。根据庇古（1929）的直觉，不完全信息在早期宏观经济理性预期文献中发挥了突出作用[见Lucas（1972）和Kydland and Prescott(1982)]。尽管有可能产生一组有趣的结果，但信息摩擦很快就从文献中消失了，这主要是由于在解决具有不完整信息的动态模型时出现的技术挑战。PI的研究将回到早期文献的范式，并检查不完全信息如何影响均衡结果。1. 本研究的学术价值在于充分说明和描述动态经济中的“信息均衡”。本研究将(1)建立信息均衡概念，为信息不完全的动态模型提供存在唯一性条件。初步结果表明，pi的均衡概念推翻了曾经被认为在具有非平凡信息动力学的模型中普遍存在的不存在结果。(2)分析表征动态理性期望模型的信息均衡空间。该方法克服了求解均衡的技术难题，通过在解析函数空间中求解模型，实现了均衡的封闭表征。(3)刻画了赋值不对称信息在均衡中生存的一类信息均衡的动态特性。pi以封闭形式表征高阶信念动态，并由于信息不完全而表现出重要的传播效应。(4)通过推导非基本移动平均表示与动态信号提取问题之间的等价关系，为移动平均表示提供了一种新的信息解释。(5)研究不完全信息如何在各种情况下改变平衡动态。初步结果表明，不完全信息在评估经济周期动态来源、确定最优信息披露和稳定政策、协调资产定价理论和数据以及提高宏观DSGE模型的经验拟合方面具有重要作用；举几个例子。2. 更广泛的影响通过关注一般情况下的动态不确定性，PI认为这些贡献可以在动态经济学的许多领域产生广泛的影响。虽然PIs研究侧重于宏观和金融，但不完全信息和解决方案技术可以更广泛地应用。拟议的研究将通过在拟议研究的概念和专业人士更熟悉的现有方法之间提供明确的联系，共同努力明确所采用技术的优点。

COLLABORATIVE RESEARCH INCOMPLETE INFORMATION IN DYNAMIC ECONOMIES:ANALYTICS AND APPLICATIONS Since the rational expectations revolution, solving for equilibria in dynamic macroeconomic models relies on imposing the restriction that model dynamics are a function of expectations of stochastic variables. Following the intuition of Pigou (1929) - that structural features of the economy coupled with incomplete information may lead to waves of pessimism and optimism -incomplete information played a prominent role in the early macroeconomic rational expectations literature [see Lucas (1972) and Kydland and Prescott (1982)]. Despite the potential for generating an interesting set of results, informational frictions soon disappeared from the literature, primarily due to the technical challenges that emerge when solving for dynamic models with incomplete information. The PI's research will return to paradigm of the early literature and examine how incomplete information affects equilibrium outcomes. 1. Intellectual Merit The intellectual merit of the proposed research is to fully specify and characterize "information equilibria" in dynamic economies. The proposed research will (1) Establish an information equilibrium concept that provides existence and uniqueness conditions for dynamic models with incomplete information. Preliminary results suggest that the PIs' equilibrium concept overturns non-existence results once thought to be pervasive in models with non-trivial informational dynamics. (2) Analytically characterize the space of information equilibria for dynamic rational expectations models. The proposed approach overcomes the technical challenges of solving for equilibrium and achieves a closed-form characterization of the equilibria by solving the model in the space of analytic functions. (3) Characterize the dynamic properties of the class of information equilibria for which endowed asymmetric information survives in equilibrium. The PIs characterize higher-order belief dynamics in closed form and demonstrate an important propagation effect due to incomplete information. (4) Provide a novel informational interpretation of moving average representations by deriving an equivalence between non-fundamental moving average representations and dynamic signal extraction problems. (5) Examine how incomplete information alters equilibrium dynamics in a variety of settings. Preliminary results suggest that incomplete information can play a vital role in assessing the sources of business cycle dynamics, determining optimal information disclosure and stabilization policies, reconciling asset pricing theory with data, and improving the empirical fit of macro DSGE models; to name a few applications. 2. Broader Impact By focusing on dynamic uncertainty in very general setups, the PI's believe the contributions can have a broad impact in many areas of dynamic economics. While the PIs research focuses on macro and finance, incomplete information and the solution techniques can be applied more broadly. The proposed research will make a concerted effort to make clear the advantages of the employed techniques by providing an explicit connection between the concepts of the proposed research and existing methodologies that are more familiar to the profession.