Econometric Methods for Structural and Semiparametric Models

摘要项目编号：0351259机构：哈佛大学项目：经济学spi: Porter， Jack标题：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。这项研究由几个项目组成。第一个项目是对英格兰和威尔士解除管制的电力公用事业市场的多单位拍卖的实证项目。投标数据和实际拍卖软件将用于估算固定成本和资本成本，这将用于分析投资激励、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动态拍卖模型估计方法，利用从投标人的优化问题中得到的一阶条件作为估计的时刻，避免了与最优出价策略的数值求解相关的计算困难。这项研究提供了与最近关于电力市场的争论相关的实证结果，并且更普遍地说，将为许多其他拍卖市场的实证分析提供新的工具。第二个项目考虑了某些半参数模型，这些模型通常使用初始“插件”条件期望估计器进行估计，并追求一种避免使用依赖于样本大小的平滑参数的估计方法。将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处理效果框架。本研究旨在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的统计实验极限理论来推广参数模型中有效点估计的标准结果。它展示了如何在更一般的条件下，包括所有规则模型和许多主要的非规则模型以及对称或非对称损失，构建到MLE的局部移位，从而产生效率。这项工作为这些模型提供了最好的估计量，这些模型是许多领域实证研究的主力。第三个项目扩展了回归不连续设计中治疗效果的半参数估计，以允许治疗分配中的未知截止。这项工作还在参数未知结构断点模型的非参数模拟中表征了估计。这种方法将进一步扩大回归不连续框架的适用性。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.