Learning with Model Uncertainty and Misspecification

学习模型的不确定性和错误指定

• 批准号: 1952874
• 负责人: In-Koo Cho
• 金额: $ 6.26万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952874&HistoricalAwards=false
• 关键词: Learning; Model; Uncertainty; Misspecification

Conventional economic theory and equilibrium concepts do not permit agents to experience doubts and ambiguity that would lead them to test the way their models of behavior are specified. Nor do they encompass alternative (sample) paths to escape from the status quo and switch among different models. These aepcets are crucial for explaining the dynamics of many types economic phenomena, particularly hwo we get to equilibrium outcomes. We propose an alternative approach to investigate the uncertainty and doubt in a dynamic context, without changing the preference of the decision maker. We aim to make theoretical and practical contributions, by developing models in which agents and economists are on an equal footing, in the sense that agents within the model confront the same doubts and ambiguity about their environment that confront an outside observer or econometrician anlyzing the data.This proposal contains three distinct projects designed to serve as laboratories for exploring the implications of endowing agents with model uncertainty and misspecification in a dynamic environment. First, the forecast combination is a way for a policy maker to aggregate dispersed information, and to hedge against model uncertainty, under the assumption that the data generating process is exogenous. We demonstrate the potential shortcomings of averaging forecasts, if the data generating process is endogenous, and develop a sensible way of combining different forecasts. Second, we investigate how how doubts can sow the seeds of financial crises. We first endogenize the heterogeneous beliefs by showing that doubts about the process generating fundamentals can produce heterogeneous beliefs and asset market bubbles. We then impose discipline on the degree of belief heterogeneity, to understand the source of recurrent bubbles. Third, a formal investigation of events generated by the presence of model uncertainty calls for a new solution concept. We plan to extend and then refine the notion of self-confirming equilibrium by focusing on the set of stochastic processes that satisfy a given bound on detection error probability rates.

传统的经济理论和均衡概念不允许代理人经历怀疑和模糊性，从而导致他们测试他们的行为模型的指定方式。它们也不包含逃离现状和在不同模式之间切换的替代（样本）路径。这些观点对于解释许多类型的经济现象的动态是至关重要的，特别是当我们达到均衡结果时。我们提出了另一种方法来调查的不确定性和怀疑在动态的背景下，不改变决策者的偏好。 我们的目标是通过开发代理人和经济学家处于平等地位的模型，在这个意义上，模型中的主体面临着与外部观察者或计量经济学家分析数据相同的关于其环境的怀疑和模糊性。该提案包含三个不同的项目，旨在作为实验室，探索赋予主体模型不确定性的含义，动态环境中的错误说明。 首先，预测组合是决策者在假设数据生成过程是外生的情况下，聚集分散信息并对冲模型不确定性的一种方式。 我们证明了平均预测的潜在缺点，如果数据生成过程是内生的，并开发一个合理的方式结合不同的预测。 第二，我们研究了怀疑如何播下金融危机的种子。 我们首先内生的异质信念表明，怀疑的过程中产生的基本面可以产生异质信念和资产市场泡沫。 然后，我们对信念异质性的程度施加纪律，以了解反复出现的泡沫的来源。 第三，一个正式的调查所产生的事件模型的不确定性的存在要求一个新的解决方案的概念。 我们计划扩展，然后完善的概念，自我确认的平衡，集中在一组随机过程，满足一个给定的边界上的检测错误概率率。