Identification, Estimation, and Applications in Auction and Differentiated Products Markets

本课题研究产业组织的两大领域（1）拍卖和（2）差异化产品需求）的实证方法及其应用。拍卖是一种重要的市场制度，它为信息不对称的市场提供了一个独特的窗口。 需求估计是微观经济学中广泛的实证研究的基础。拍卖方面的工作包括当不可观察的因素影响所观察到的拍卖样本、参与拍卖的投标人集合以及他们的出价时，对第一价格拍卖估计的方法学研究。我们提供了新的识别结果，一个新的估计方法，和模拟研究来评估的方法。该项目还包括对美国外大陆架的联邦矿产租赁拍卖进行实证研究。 在过去的25年里，技术和政策发生了几次重大变化，可能会对这些销售产生重大影响。 我们研究了这些变化对投标人参与，投标，钻井和生产的影响。我们研究了三维成像技术对这些拍卖的信息结构和出价的影响，并评估了有争议的特许权使用费减免计划。 差异化产品的需求工作由三个部分组成，每个部分都研究在比以前通常考虑的假设更弱的假设下可以学到什么。第一个考虑的情况下，消费者一级的特点和选择是可观察的（微观数据）。 偏好模型是一种非参数、无分布的嵌套随机系数离散选择模型，具有参数函数形式和分布假设。它包含了特定于选择的不可观察因素，以及一个非常灵活的关于偏好如何在产品和个人之间变化的规范。这项工作的这一部分提供了对获得可靠估计所需的数据和假设的更深入理解。第二个项目考虑的情况下，一个观察只有市场层面的数据。 这种情况更具挑战性，需要不同类型的参数;然而，我们再次获得积极的结果，限制较少的模型比以前考虑的。 第三个项目审查需求估计，考虑到当公司选择提供的产品时，这导致产品的可观察和不可观察特征之间的相关性。 我们提供识别的条件，然后开发，评估和应用新的估计方法。 这项工作的方法论部分涉及从反托拉斯政策到教育经济学和公共资源分配等领域的经济和政策问题分析的重要问题。 一些实证工作解决的问题，直接重要的政府政策在美国的工作将通过研究研讨会，会议演示和研究文章的出版广泛传播。 这项工作将有助于培养研究生，他们将担任研究助理和共同作者。

This project involves research on empirical methods and applications in two broad areas of industrial organization: 1) auctions and 2) demand for differentiated products. Auctions are important market institutions and provide a unique window on strategic behavior in markets with asymmetric information. Demand estimation is fundamental to a wide range of empirical work in microeconomics.The work on auctions includes methodological research on estimation of first-price auctions when unobservables affect the sample of auctions observed, the set of bidders participating, and the bids they make. We provide new identification results, a new estimation approach, and simulation studies to evaluate the methods. The project also includes an empirical examination of Federal mineral lease auctions on the U.S. Outer-Continental Shelf. Over the last 25 years there have been several major changes in technology and policy that are likely to have significant effects on these sales. We examine the effects of these changes on bidder participation, bidding, drilling, and production. We study the effects of three-dimensional imaging technology on the information structure and bidding at these auctions, and also assess the controversial royalty relief program. The work on demand for differentiated products is comprised of three components, each examining what can be learned under weaker assumptions than those typically considered previously. The first considers the case in which consumer-level characteristics and choices are observable (micro data). The model of preferences is nonparametric and distribution free, nesting random coefficients discrete choice models widely used in practice with parametric functional form and distributional assumptions. It incorporates choice-specific unobservables and a very flexible specification of how preferences vary across products and individuals. This component of the work provides a deeper understanding of the data and assumptions required to obtain reliable estimates. The second project considers the case in which one observes only market level data. This case is more challenging and requires a different type of argument; however, we again obtain positive results for less restrictive models than those previously considered. The third project examines demand estimation allowing for the fact that when firms choose the products to offer, this leads to correlation between observable and unobservable characteristics of products. We provide conditions for identification, then develop, evaluate, and apply new estimation methods. The methodological components of this work addresses issues of importance to the analysis of economic and policy questions in fields ranging from anti-trust policy to the economics of education and the allocation of public resources. Some of the empirical work addresses issues of direct importance to government policy in the U.S. The work will be disseminated broadly through research seminars, conference presentations, and publication of research articles. This work will contribution to the training of graduate students, who will serve both as research assistants and co-authors.