Econometric Methods for Structural Models

结构模型的计量经济学方法

• 批准号: 0112095
• 负责人: Jack Porter
• 金额: $ 11.53万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-15 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0112095&HistoricalAwards=false
• 关键词: Econometric; Methods; Structural; Models

This proposal contains projects that develop statistical econometric methods for certain economic models of interest. The econometric issues considered include estimation in dynamic auctions, optimal estimation of procurement auction and job search models, and estimation of certain kinds of economic shifts or breaks. A substantial empirical project on daily auctions in the deregulated electric utility market in England and Wales applies some of the econometric tools. Our estimates are used to determine profitability and examine the incentives to invest in new capacity. Our model and estimates could also be used to consider outcomes associated with alternative auction designs or to analyze possible collusion among suppliers in this market For the empirical project on multi-unit electricity auctions, we have obtained a license to use the actual software used by the auctioneer to determine price and quantity outcomes (given bids), and we are also obtaining the three most recent years of data. With software and data in hand, we use an equilibrium model of firm supplier behavior to estimate start-up and fixed costs for each generator. These costs will then be used to examine profits and investment incentives. We will also incorporate a cost of capital approach to maintenance and availability decisions.The estimation method to be used in the empirical electricity auction project is developed in detail due to its more general applicability in a variety of auction settings. Similar in spirit to an Euler equation approach, our econometric method uses the first order conditions as the basis for moments of estimation. This approach avoids the computational difficulty associated with numerically solving for optimal bidding strategies. The technique extends the approach of Berry and Pakes (2000), which was designed for estimation of oligopolistic models, to work in dynamic auctions. A key challenge to a first order condition approach in auctions is the inherent discontinuity in the firm profit function. This discontinuity leads to a nondifferentiability that makes approximation of the first order condition difficult. We overcome this difficulty with a semiparametric smoothing technique and obtain parameter estimates at a nonparametric rate. Next, we consider parameter-dependent support models which include certain auction models as well as production frontier models, and some search models. While estimation in these models has been studied broadly, we examine what estimators are most efficient. We use LeCam's theory of statistical experiments to show that the maximum likelihood estimators are not asymptotically efficient in such nonregular models, while Bayes' estimators are. In another project, efficient estimation in structural break models is studied. Again a limit of experiments approach will be used to explore efficiency and other issues in traditional unknown change point models. Another project considers estimation of breaks in a nonparametric regression curve model. Such estimates are useful in certain regression discontinuity settings.

该提案包括为某些感兴趣的经济模型开发统计计量经济学方法的项目。考虑的计量经济学问题包括估计在动态拍卖，采购拍卖和求职模型的最优估计，并估计某些类型的经济转变或休息。在英格兰和威尔士放松管制的电力公用事业市场的日常拍卖的大量实证项目应用的一些计量经济学工具。我们的估计用于确定盈利能力，并检查投资新产能的激励措施。我们的模型和估计也可以用来考虑与替代拍卖设计相关的结果，或分析供应商在这个市场中可能的勾结 对于多单元电力拍卖的实证项目，我们已经获得了使用拍卖师使用的实际软件来确定价格和数量结果（给定出价）的许可，我们还获得了最近三年的数据。有了软件和数据在手，我们使用一个均衡模型的公司供应商的行为来估计启动和固定成本为每个发电机。然后，这些成本将用于审查利润和投资激励措施。我们还将纳入资本成本的方法来维护和可用性decision.The估计方法将用于实证电力拍卖项目的详细开发，由于其更普遍的适用性，在各种拍卖设置。类似的精神欧拉方程的方法，我们的计量经济学方法使用的一阶条件作为估计的时刻的基础。这种方法避免了与数值求解最优投标策略相关的计算困难。该技术扩展了Berry和Pakes（2000）的方法，该方法是为估计寡头垄断模型而设计的，适用于动态拍卖。拍卖中一阶条件方法的一个关键挑战是公司利润函数的内在不连续性。这种不连续性导致不可微性，使得一阶条件的近似困难。我们克服了这个困难，半参数平滑技术，并获得非参数率的参数估计。 接下来，我们考虑参数依赖的支持模型，其中包括某些拍卖模型以及生产前沿模型，和一些搜索模型。虽然这些模型中的估计已经被广泛研究，我们研究什么估计是最有效的。我们使用LeCam的统计实验理论来证明，在这种非正则模型中，最大似然估计量不是渐近有效的，而贝叶斯估计量是。 在另一个项目中，研究了结构突变模型的有效估计。再次，有限的实验方法将被用来探索传统的未知变点模型的效率和其他问题。另一个项目考虑了非参数回归曲线模型中突变点的估计。这种估计在某些回归不连续性设置中很有用。