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Economic Models with Infinitely Many Commodities

Economic Models with Infinitely Many Commodities
具有无限多种商品的经济模型
批准号:
8720966
负责人:
William Zame
金额:
$5.9万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1988
资助国家:
美国
项目状态:
已结题
起止时间:
1988-05-01 至 1990-10-31

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中文摘要
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英文摘要
This project develops new economic models of markets with an infinite number of commodities. These models are used to study monopolistic competition with large numbers of firms, the dynamics of financial markets under uncertainty, the foundations of mathematical models of markets and other difficult problems in mathematical economic theory. More specifically, in the first part of the project a model is developed in which firms are sufficiently small so that each firm's effect on other firms is negligible, yet in equilibrium, active firms charge more than the perfectly competitive price and may make positive monopoly profits. This model would be more realistic than existing economic models in which the monopoly power of firms disappears as they become small in comparison to the market. The second part consists of the development of continuous time stochastic models of incomplete markets. Stochastic models with incomplete markets are useful because in the real world it is impossible to completely insure against uncertainty because many futures and contingent claims markets are in fact missing. The continuous time framework allows the investigator to relate his work to financial models and to address such questions as: does frequent trading lead to efficiency?
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会议论文
Collaborative Research: Experiments on Information and Information Processing in Financial Markets
  • 批准号:
    0617027
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $16.92万
  • 财政年份:
    2006
  • 负责人:
    William Zame
  • 依托单位:
Doctoral Dissertation Research in Economics: The Informational Role of Social Comparisons: An Experimental Study
Contracts and Markets: Toward a General Equilibrium Theory of Firms
Collaborative Research: The Evolution of Prices and Allocations in Markets: Theory and Experiment
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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