Learning, Credibility and Asset Markets

学习、信誉和资产市场

• 批准号: 8907999
• 负责人: David Levine
• 金额: $ 8.3万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-09-01 至 1991-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8907999&HistoricalAwards=false
• 关键词: Learning; Credibility; Asset; Markets

The goal of this project is to understand, both in a general theoretical way, and in concrete and empirically relevant economic problems, asset market equilibrium. Algorithms for computing equilibria in models with a mixture of private and social risk were programmed under the previous grant. They are used in this product to study a new class of models with limited insurance possibilities: debt constrained asset markets. Liquidity constraints emerge in this model from the assumption that it is not possible to force borrowers to service or pay off debts from their future income. Traders may be denied credit, and their assets may be attached for the payment of past debts. This is a reasonable representation of the way in which actual asset markets work. Debtor prison no longer exists, and indeed, modern bankruptcy laws make it possible for traders to preserve some assets even in the face of bankruptcy laws. Debt constrained markets could explain why insurance markets are incomplete in real world economies. Research is continued on the dynamics of asset distribution models. This project relaxes the assumption of a representative economic agent made in real business cycle models. Numerical simulation on the supercomputer is used to determine the global picture of the set of equilibria of asset distributions with a mix of private and social risk. The project develops and tests methods of accurately approximating these asset distribution. Methods developed in the previous project to study reputation and learning in repeated games are extended and used to study learning and approximate knowledge in asset markets. This is important because it permits rigorous analysis of asset market behavior that can't otherwise be explained. Most current theories either assume common knowledge or assume behavior that is so stupid that it is hard to imagine even a naive agent behaving this way. In sum, this research develops powerful new methodological tools for studying market dynamics with heterogeneous agents and for rigorously modelling markets in which agents make mistakes but learn from their mistakes. These tools are used to study realistic models of asset markets in which certain risks can not be insured or can be insured at prohibitive rates.

这个项目的目标是了解，无论是在一般 理论方法，并在具体和经验相关 经济问题资产市场均衡 算法 计算模型中的均衡， 社会风险已列入先前赠款的方案。 他们是 用于研究一类新的模型， 保险的可能性：债务约束的资产市场。 流动性约束出现在这个模型从假设 不可能强迫借款人提供服务或还清债务， 从未来的收入中扣除债务。 商人可能会被拒绝信贷， 他们的资产可能会被扣押以偿还过去的债务。 这是一个合理的代表性的方式， 资产市场运作。 债务人监狱不再存在，事实上， 现代破产法使商人有可能 即使面对破产法，也要保留一些资产。 债务 市场约束可以解释为什么保险市场 在真实的世界经济中是不完整的。 继续研究资产分配的动态 模型 这个项目放宽了代表的假设 经济主体在真实的商业周期模型中。 数值 超级计算机上的模拟用于确定全局 资产分布均衡集的图像， 私人和社会风险的混合。 该项目开发和测试 准确地近似这些资产分布的方法。 在以前的项目中开发的研究声誉和 在重复游戏中学习被扩展并用于研究 资产市场的学习和近似知识。 这是 重要的是，它允许对资产市场进行严格的分析 无法用其他方式解释的行为 最新 理论要么假设常识，要么假设行为， 是如此的愚蠢，以至于很难想象一个天真的代理人 这样的行为。 总之，这项研究开发了强大的新方法工具 用于研究具有异质代理的市场动态， 严格地模拟市场，其中代理人会犯错误， 从错误中吸取教训 这些工具是用来研究 资产市场的现实模型，其中某些风险不能 被保险或可以以令人望而却步的费率投保。