Econometric Methods for Structural and Semiparametric Models

摘要提案编号：0351259机构：哈佛大学NSF研究所：经济学PI： 波特，杰克RTITLE：结构和半参数模型的计量经济学方法本研究涉及各种经济结构和半参数模型的计量经济学方法。该研究由几个项目组成。 第一个项目是在英格兰和威尔士放松管制的电力公用事业市场的多单元拍卖的实证项目。投标数据和实际拍卖软件将用于估计固定成本和资本成本，这些成本将用于分析投资动机、拍卖设计问题和可能的串通行为。同伴工作开发了一种新的方法来动态拍卖模型估计使用的一阶条件来自投标人的优化问题作为估计的时刻，并避免了计算困难与数值求解最优投标策略。这项研究提供了有关最近的辩论有关电力市场的实证结果，更普遍的是，将提供新的工具，许多其他拍卖markets.The第二个项目的实证分析认为，通常使用初始的“插件”条件期望估计估计的某些半参数模型，并追求一种方法来估计，避免使用样本大小依赖平滑参数。最近邻方法与局部多项式回归相结合，即使在高维协变量空间中，也能充分降低根n一致性的偏倚率。主要适用的模型包括部分线性模型和平均治疗效果框架。本研究的目的是提供计量经济学工具，便于实证研究人员在一类半参数模型中应用。第三个项目使用Le Cam的统计试验极限理论推广了参数模型中有效点估计的标准结果。它展示了如何构建一个局部转移到MLE，在更一般的条件下产生效率，包括所有常规和许多领先的非常规模型沿着对称或非对称损失。这项工作提供了最好的估计，在这些模型中，这是在许多领域的实证研究的主力。 第三个项目扩展了回归不连续设计中治疗效应的半参数估计，以允许治疗分配中的未知截止值。这项工作还表征了参数未知结构断点模型的非参数模拟估计。 这种方法将进一步扩大回归不连续性框架的适用性。这些项目旨在为应用研究人员提供新的计量经济学工具。

ABSTRACTPROPOSAL NO: 0351259INSTITUTION: Harvard UniversityNSF PROGRAM: EconomicsPI: Porter, Jack RTITLE: Econometric Methods for Structural and Semiparametric ModelsThis research deals with econometric methods for various economic structural and semiparametric models. The research consists of several projects. The first project is an empirical project on multi-unit auctions in the deregulated electric utility market in England and Wales. Bid data and the actual auction software will be used to estimate fixed costs and costs of capital, which will be used to analyze incentives to invest, auction design issues, and possible collusive behavior. Companion work develops a new approach to dynamic auction model estimation using the first order condition derived from a bidder 's optimization problem as a moment for estimation and avoiding the computational difficulty associated with numerically solving for optimal bidding strategies. This research provides empirical results relevant to the recent debates concerning electricity markets, and, more generally, will provide new tools for the empirical analysis of many other auction markets.The second project considers certain semiparametric models that are typically estimated using an initial "plug-in "conditional expectation estimator and pursues an approach to estimation that avoids using a sample size dependent smoothing parameter. A nearest neighbor approach is combined with local polynomial regression to achieve sufficient bias-rate reduction for root n consistency even with a high dimensional covariate space. Leading applicable models include the partially linear model and the average treatment effect framework. This research is aimed at providing econometric tools that are easy for the empirical researcher to apply within a class of semiparametric models. The third Project uses Le Cam 's limits of statistical experiments theory to generalize standard results on efficient point estimation in parametric models. It shows how to construct a local shift to the MLE that yields efficiency under more general conditions, including all regular and many leading nonregular models along with symmetric or asymmetric loss. This work provides best estimators in these models, which are the workhorse of empirical research in many fields. The third project extends semiparametric estimation of treatment effects in the regression discontinuity design to allow for an unknown cut-off in treatment assignment. This work also characterizes estimation in a nonparametric analog of the parametric unknown structural breakpoint model. This approach will further broaden the applicability of the regression discontinuity framework. These projects are aimed at providing new econometric tools for the applied researcher.