Econometric methods for Moment Restriction Models and Mixtures

力矩限制模型和混合的计量经济学方法

• 批准号: 0551271
• 负责人: Yuichi Kitamura
• 金额: --
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0551271&HistoricalAwards=false
• 关键词: Econometric; methods; Moment; Restriction; Models

This project consists of two parts. The first part is concerned with estimation of momentcondition models. A new estimation method that achieves an asymptotic minimax efficiencyproperty is proposed. The second part explores nonparametric identifiability for finite mixturemodels, and develops a semiparametric estimator.Intellectual merit of the proposed activitiesThe first part aims at providing a method for estimating moment condition models, whichare usually estimated by the Generalized Method of Moments (GMM) in the current appliedliterature. This research is motivated by the recent concerns that the performance of GMMmay be problematic under empirically relevant data generating processes. The conventionallocal first-order efficiency theory is of limited use for analyzing this problem. This part of theproposal develops a global first-order efficiency theory for moment condition models using thelarge deviation principle (LDP), which is a major subject in probability theory. It then proposesa new estimator that achieves the asymptotic minimax efficiency bound in a large deviationsense. The new estimator uses Owens empirical likelihood as a crucial ingredient; indeed, itcan be interpreted as a robustified version of the conventional empirical likelihood estimator.Preliminary simulation results imply that the new method outperforms competing estimators,though more experimental studies will be undertaken in the project. Empirical applications ofthe method to panel data models and further theoretical extensions of the method are planned.The second part considers finite mixture models. Finite mixture models allow appliedresearchers to deal with parameter variations, such as unobserved heterogeneity or unobservedregimes, in a convenient and interpretable way. The existing literature on finite mixture models,however, focuses on parametric models. The proposed research develops nonparametricidentification theory for finite mixtures, where components of a mixture model are treatednonparametrically. While a few studies have succeeded in treating such models, they demandspecifications such as symmetric distributions or multiple independent observations from eachobservation unit. This research takes an approach that is very different from these studies andshows that nonparametric identification is possible by using variations in covariates. It also proposesa new estimation algorithm for a semiparametric finite mixture model. Generalizations ofthese results, including their extensions to dynamic models, will be explored.Broader impacts of the proposed activitiesThe broader impacts of the results from the proposed activities include the following. First,the project will produce computer programs that will be made accessible to public. For example,the research on minimax estimation will yield computer software for the new procedure. Thiseffort, as a byproduct, will include developing a reliable program for implementing empiricallikelihood methods, which have been attracting a great deal of attention among practitioners.This will benefit a broad range of communities. Second, graduate students will be supporteddirectly and indirectly by the proposed project. Past NSF support enabled graduate students toparticipate in projects of the PI as research assistants. This provided them with financial supportas well as opportunities to learn state-of-the-art econometrics and programming skills. Theproposed project will continue to support graduate students and provide them with the skills andknowledge necessary for their thesis research and early career development. Third, econometricmethodologies from this project will be used to analyze empirical economic problems. Thisincludes applications of the new minimax estimation method to models of income dynamics,which have important policy implications. The proposed methods are expected to shed newlight on economic problems that are highly relevant to a broad audience.

本项目由两部分组成。第一部分是关于矩条件模型的估计。提出了一种新的估计方法，实现了一个渐进的minimax efficiency属性。第二部分研究了有限混合模型的非参数可辨识性，并提出了一种半参数估计方法。这项研究的动机是最近的关注，GMM的性能可能是有问题的经验相关的数据生成过程。传统的局部一阶有效性理论在分析这一问题时作用有限。这部分的建议发展了一个全球性的一阶效率理论的矩条件模型使用大偏差原则（LDP），这是一个主要的问题，在概率论。然后提出了一个新的估计量，它在大偏差意义下达到了渐近极大极小效率界。新的估计量使用欧文斯经验似然作为一个关键成分;事实上，它可以被解释为一个鲁棒版本的传统经验似然估计。初步的模拟结果表明，新方法优于竞争估计，虽然更多的实验研究将在该项目中进行。计划将该方法应用于面板数据模型的实证研究和进一步的理论扩展。第二部分考虑有限混合模型。有限混合模型允许appliedresearchers处理参数的变化，如未观察到的异质性或unobservedregimes，在一个方便和可解释的方式。然而，现有的文献有限混合模型，侧重于参数模型。该研究发展了有限混合物的非参数识别理论，其中混合物模型的组分被非参数化处理。虽然一些研究已经成功地处理了此类模型，但它们需要诸如对称分布或每个观察单元的多个独立观察等规范。这项研究采取了一种与这些研究截然不同的方法，表明通过使用协变量的变化，非参数识别是可能的。并提出了半参数有限混合模型的一种新的估计算法。这些结果的推广，包括其扩展到动态模型，将被探讨。拟议活动的更广泛的影响拟议活动的结果的更广泛的影响包括以下内容。首先，该项目将产生计算机程序，将提供给公众。例如，对极小极大估计的研究将产生新程序的计算机软件。作为副产品，这一成果将包括开发一个可靠的程序来实施危险可能性方法，该方法已经吸引了从业者的大量关注，这将使广泛的社区受益。第二，研究生将直接或间接地被所提出的项目所吸引。过去的NSF支持使研究生能够作为研究助理参与PI的项目。这为他们提供了财政支持以及学习最先进的计量经济学和编程技能的机会。拟议的项目将继续支持研究生，并为他们的论文研究和早期职业发展提供必要的技能和知识。第三，本项目的计量经济学方法将用于分析实证经济问题。这包括应用新的极小极大估计方法的收入动态模型，这具有重要的政策意义。所提出的方法有望为与广大受众高度相关的经济问题提供新的思路。