Nonparametric and Robust Methods in Econometrics

计量经济学中的非参数和稳健方法

• 批准号: 0851759
• 负责人: Yuichi Kitamura
• 金额: $ 26.41万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0851759&HistoricalAwards=false
• 关键词: Nonparametric; Robust; Methods; Econometrics

This project consists of two parts. The first is concerned with random coefficients discrete choice models. The second explores robust and efficient estimation of moment restriction models. The first part of the project aims at developing a new estimator for a nonparametric random coefficient binary choice model. Random coefficient binary choice models are widely used in applied economic analysis. They offer a natural and convenient framework for modeling economic decision making in the presence of unobserved heterogeneity. Typically researchers make parametric distributional assumptions on the error term and the random coefficients distribution, then apply the maximum likelihood estimator (MLE), often with numerical or simulation methods that can be computationally costly. The goal of the proposed research is to provide a computationally attractive nonparametric estimator that avoids ad hoc distributional assumptions. The resulting estimator requires neither numerical optimization nor numerical or simulation-based integration, and has desirable properties in terms of its rate of convergence and asymptotic normality properties.The second part of the project is concerned with robust estimation of a moment restriction model. The model is semiparametric and distribution-free, therefore imposes mild assumptions. Yet it is reasonable to expect that the probability law of observations may have some deviations from the ideal distribution as modeled by the moment restriction model. It is then sensible to seek estimation and testing procedures that are robust against slight perturbations in the probability measure that generates observations. The main result shows that an estimator, termed the moment restriction minimum Hellinger distance estimator (MHDE) in this project, possesses optimal minimax robust properties. Moreover, it remains semiparametrically efficient when the model assumptions hold. Convenient numerical algorithms for implementing them are provided. Extensions of the results to time series data are considered.The broader impacts of the proposed activities include the following. First, the project aims at developing tools that are useful for applied researchers across a wide range of fields in economics but also in other disciplines in social science. For example, random coefficient discrete choice models are important in many areas including marketing, political science and other social sciences. Likewise, the robust estimation method is concerned with moment restriction models and therefore applicable to numerous models in economics and finance. The two projects develop practical algorithms, which will make the procedures feasible tools for practitioners. Second, the project will yield computer programs for the proposed procedures, written inMATLAB, and they will be made freely available to the public. Also, the project for the robust estimation procedure includes the development of a suite of programs in STATA as well. Providing STATA codes for the moment restriction MHDE, EL and other recent methods for moment condition models is potentially beneficial for applied researchers. Third, the proposed activities are expected to provide educational benefits to graduate students through research assistantships supported by the proposed grant. My previous grants provided by the NSF supported a number of graduate students. This support gave them valuable opportunities to develop their skills in various areas including computer programing and research planning, which proved helpful in developing their own thesis topics. These experiences will have invaluable impacts on their research careers in academia or the public sector. This project enables graduate students to participate in the planned projects, which will promote their dissertation research.

本项目由两部分组成。第一个是随机系数离散选择模型。第二部分探讨矩约束模型的稳健有效估计。 第一部分的目的是发展一个新的估计的非参数随机系数二元选择模型。随机系数二元选择模型在应用经济分析中有着广泛的应用。它们为在存在未观察到的异质性的情况下进行经济决策建模提供了一个自然而方便的框架。通常，研究人员对误差项和随机系数分布进行参数分布假设，然后应用最大似然估计（MLE），通常使用计算成本很高的数值或模拟方法。建议的研究的目标是提供一个计算吸引力的非参数估计，避免特设的分布假设。由此产生的估计既不需要数值优化，也不需要数值或模拟为基础的集成，并在其收敛速度和渐近正态properties.The第二部分的项目是关注的时刻限制模型的鲁棒估计方面具有理想的性能。该模型是半参数和分布自由，因此施加温和的假设。 然而，可以合理地预期，观测值的概率律可能与矩约束模型所模拟的理想分布有一些偏差。因此，明智的做法是寻求对产生观测结果的概率测量中的微小扰动具有鲁棒性的估计和检验程序。主要结果表明，矩约束最小Hellinger距离估计（MHDE）具有最优的Minimax鲁棒性质。此外，它仍然是半参数有效的模型假设成立时。方便的数值算法来实现它们。考虑将结果扩展到时间序列数据，拟议活动的更广泛影响包括以下方面。首先，该项目旨在开发对经济学广泛领域以及社会科学其他学科的应用研究人员有用的工具。例如，随机系数离散选择模型在许多领域都很重要，包括市场营销，政治科学和其他社会科学。同样，稳健估计方法涉及矩约束模型，因此适用于经济和金融中的许多模型。这两个项目开发实用的算法，这将使程序可行的工具，为从业人员。第二，该项目将产生计算机程序的建议程序，写在MATLAB，他们将免费提供给公众。此外，该项目的稳健估计程序包括一套程序的STATA以及发展。为矩约束MHDE、EL和矩条件模型的其他最新方法提供STATA代码对应用研究人员可能是有益的。第三，拟议的活动预计将通过拟议赠款支持的研究助理奖学金为研究生提供教育福利。我以前由NSF提供的赠款支持了一些研究生。这种支持给了他们宝贵的机会，发展他们的技能，在各个领域，包括计算机编程和研究规划，这证明有助于发展自己的论文题目。 这些经验将对他们在学术界或公共部门的研究生涯产生宝贵的影响。该项目使研究生能够参与计划中的项目，这将促进他们的论文研究。