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

动态经济中的合作研究：分析和应用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限制，即模型动态是随机变量预期的函数。根据庇古(1929)的直觉--经济的结构特征加上不完全信息可能导致悲观和乐观浪潮--不完全信息在早期宏观经济理性预期文献中发挥了重要作用[见卢卡斯(1972)和基德兰和普雷斯科特(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，主要是因为在求解不完全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，并考察不完全信息如何影响均衡结果。智力价值拟议研究的智力价值在于充分明确和刻画动态经济中的“信息均衡”。提出的研究将(1)建立一个信息均衡概念，为不完全信息动态模型提供存在唯一性条件。初步结果表明，PI的均衡概念颠覆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)解析地刻画了动态理性预期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，实现了均衡的闭式刻画。(3)刻画了具有不对称信息的信息均衡的动态特性。PI刻画了闭合形式的高阶信任动态，并展示了由于不完全信息而产生的重要传播效应。(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性，给出了移动平均表示的一种新的信息解释。(5)研究了不完全信息如何改变各种设置下的均衡动态。初步结果表明，不完全信息可以在评估商业周期动态的来源、确定最优信息披露和稳定政策、使资产定价理论与数据相一致、以及改进宏观动态随机序列均衡模型的经验匹配等方面发挥重要作用。更广泛的影响通过关注非常普遍的设置中的动态不确定性，PI相信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的研究主要集中在宏观和金融领域，但不完全信息及其解决技术可以得到更广泛的应用。拟议的研究将作出协调一致的努力，通过在拟议的研究的概念与该专业更熟悉的现有方法之间建立明确的联系，从而明确所采用的技术的优势。

COLLABORATIVE RESEARCHINCOMPLETE INFORMATION IN DYNAMIC ECONOMIES:ANALYTICS AND APPLICATIONSSince 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 MeritThe 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 empiricalfit of macro DSGE models; to name a few applications.2. Broader ImpactBy 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.