Monte Carlo Simulation - Applications in Econometrics and Economic Modeling

本研究主要探讨两个议题：（1）不对称投标人， 第一价格拍卖中的投标人联盟;（2）动态 潜在变量和非均衡模型。 这些主题共享 分析上难以处理的特性，因此， 需要数值技术进行研究。 硬件和 软件方法已经对这些进行了计算方法 可行的问题。 第一个主题很重要的一个原因是 联邦政府广泛使用第一价格拍卖 用于商品的销售和采购。 然而，尽管如此， 人们对联合的可行性知之甚少 在这些拍卖会上。 如果所有投标人都相同，如果 投标人的联盟是可能的，那么他们的出价源于 来源，与来源根本不同。 非串通投标人的投标。 因此，第一步 分析投标人串通是为了了解行为， 行为不合作的异质投标人。 解决方案将 一级价格拍卖模型和结果 用于了解和描述情况下， 投标人可以从中获利。 第二个主题之所以重要， 变量技术可用于估计美国的模型。 货币需求，其中一些重要的变量是固有的 潜在（或未观察到）和动态。 这些变量的例子 是预期的和永久的收入，通常是衡量金钱的标准， 本身 研究人员开发了一种技术， 数值模拟未观察到的变量， 在不去除潜在变量的情况下得出推论。

This research focuses on two topics: (1) Asymmetric bidders and coalitions of bidders at first price auctions and, (2) Dynamic latent variables and disequilibrium models. The topics share the characteristic of being analytically intractable and, therefore, requiring numerical techniques for investigation. Hardware and software methods have made a computational approach to these problems feasible. One reason the first topic is important is that the federal government uses first price auctions extensively for both the sale and procurement of commodities. Nevertheless, very little is understood about the feasibility of coalitions forming at these auctions. If all bidders are identical and if coalitions of bidders are possible, then their bids stem from a source that is fundamentally different from the source for the bids of non-colluding bidders. Consequently, a first step in analyzing bidder collusion is to understand the behavior of heterogeneous bidders who act non-cooperatively. Solutions will be obtained for first price auction models and the results utilized to understand and characterize the circumstances under which bidders can profitably collude. One reason the second topic is important is because the latent variable technique can be used in estimating models of the U.S. money demand where a number of important variables are inherently latent (or unobserved) and dynamic. Examples of these variables are expected and permanent income and often the measure of money, itself. The investigators have developed a technique for numerically simulating the unobserved variables that permits inferences to be drawn without removing the latent variables.