Model Uncertainty and Macrodynamics

这个项目涉及四项宏观经济动态研究，都是在模型不确定的情况下做出决定的背景下进行的。第一个涉及扩展作者先前在稳健决策和均衡理论方面的工作。在这种情况下，模型的不确定性促使决策者将不同模型决策的稳健性偏好结合在一起。其目的是扩展奈特不确定性对高风险证券价格影响的特征。第二项研究分析了适应性学习作为循环动力的一个来源。作者发展并应用了一类自适应模型在远离理性预期均衡的情况下表现出的循环动力学理论。第三项研究涉及发现管理不同面额货币适当供应的关键技术原则，作为社会学习的历史案例研究。最后一项研究使用隐藏周期性的计量经济学模型作为实验室，在实验室中评估信念异质性(即理性信念与信念融合)的相互竞争的理论模型。前两项研究和最后一项研究以不同的方式结合在一起，包括使用常用的计量经济学和数学方法来表征和操纵模型之间的距离。

This project involves four studies of macroeconomic dynamics all set in contexts where decisions are made under model uncertainty. The first involves extending the authors previous work on robust decision and equilibrium theory. Here, model uncertainty induces decision makers to incorporate a preference for robustness of decisions across models. The aim is to extend characterizations of the effects of Knightian uncertainty on prices of risky securities. The second study analyzes adaptive learning as a source of recurrent dynamics. The author develops and applies a theory of the recurrent dynamics that a class of adaptive models displays away from rational expectations equilibria. The third study involves the discovery of key technical principles governing the proper supply of monies of different denominations as an historical case study of social learning. The final study employs the use of an econometric model of hidden periodicity as a laboratory in which to evaluate competing theoretical models of heterogeneity of beliefs (i.e., rational beliefs versus merging of beliefs). The first two studies and the last one are united in various ways, including the use of common econometric and mathematical methods used to characterize and manipulate the distance between models.