Econometric Methods for Structural and Semiparametric Models

NSF计划：EconomicsPI：Porter，Jack RTITLE：结构和半参数模型的计量经济方法这项研究讨论了各种经济结构和半参数模型的计量方法。这项研究由几个项目组成。第一个项目是关于英格兰和威尔士放松管制的电力公用事业市场中的多单位拍卖的经验性项目。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。伴随工作发展了一种新的动态拍卖模型估计方法，利用投标人S优化问题的一阶条件作为估计时刻，避免了与数值求解最优投标策略相关的计算困难。这项研究提供了与最近有关电力市场的辩论相关的经验结果，更广泛地说，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的“插件”条件期望估计器进行估计，并寻求一种避免使用依赖于样本量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获得足够的根n一致性偏差率降低。主要的适用模型包括部分线性模型和平均治疗效果框架。本研究旨在提供计量经济学工具，便于实证研究者在一类半参数模型中应用。第三个项目使用Le Cam的S统计实验极限理论来推广关于参数模型中有效点估计的标准结果。它展示了如何构造局部转移到最大似然估计，在更一般的条件下产生效率，包括所有规则和许多主要的非规则模型以及对称或非对称损失。这项工作在这些模型中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。第三个项目在回归间断设计中扩展了治疗效果的半参数估计，以允许治疗分配中的未知截止。这项工作还刻画了参数未知结构断点模型的非参数模拟的估计。这一做法将进一步扩大回归间断框架的适用范围。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.