Collaborative Research: Flexible and Robust Data-driven Inference in Nonparametric and Semiparametric Econometrics

协作研究：非参数和半参数计量经济学中灵活且稳健的数据驱动推理

• 批准号: 1459931
• 负责人: Matias Cattaneo
• 金额: $ 19万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1459931&HistoricalAwards=false
• 关键词: Collaborative; Research; Flexible; Robust; Data

This award funds research that will develop new statistical methods for use in analyzing economic data and testing economic theory. The goal is to develop new procedures that are more robust (i.e. less sensitive) to the specifics of their implementation. The project advances science because the new methods will give economists and other social scientists better methods for statistical testing of economic models. Drawing valid inferences from available data is important as well in a variety of policy settings. The ideal method would be flexible, simple, and robust in the sense that the results would not be overly dependent on the specific way the method is applied in any given context. While previous work in econometrics has developed new nonparametric and semiparametric methods to meet the first two goals, these methods are not very robust. Their use in practice requires choices whose effect in finite samples is not known. As a result, many empirical social scientists employ parametric methods. By developing new nonparametric and semiparametric methods that are robust, the PIs hope to develop methods that avoid misspecification bias while also being attractive for practical use.They plan to employ an alternative asymptotic theory to derive novel distributional approximations for non-and semi-parametric statistics. They plan four lines of research: (i) alternative asymptotic results and bootstrapping validity for non-linear semiparametrics; (ii) alternative asymptotic results and consistent standard-errors for linear semiparametrics, (iii) higher-order expansions for the alternative asymptotic results, and (iv) new robust nonparametric and semiparametric methods employing local-polynominal techniques.

该奖项资助的研究将开发新的统计方法，用于分析经济数据和测试经济理论。 目标是制定新的程序，使其对执行的具体情况更加健全（即不那么敏感）。 该项目推动了科学的发展，因为新方法将为经济学家和其他社会科学家提供更好的方法来对经济模型进行统计测试。 在各种政策设置中，从可用数据中得出有效的推论也很重要。 理想的方法应该是灵活、简单和鲁棒的，在这个意义上，结果不会过度依赖于该方法在任何给定环境中的具体应用方式。 虽然以前的工作在计量经济学已经开发了新的非参数和半参数的方法，以满足前两个目标，这些方法是不是很强大。 它们在实践中的使用需要选择，其在有限样本中的效果是未知的。 因此，许多经验社会科学家采用参数方法。 通过开发新的非参数和半参数方法，是强大的，PI希望开发的方法，避免误设定偏差，同时也是有吸引力的实际用途。他们计划采用替代渐近理论，以获得新的非和半参数统计分布近似。 他们计划四条研究路线：（i）非线性半参数的替代渐近结果和自举有效性;（ii）线性半参数的替代渐近结果和一致的标准误差，（iii）替代渐近结果的高阶展开，以及（iv）采用局部多项式技术的新的鲁棒非参数和半参数方法。

项目成果

Large sample properties of partitioning-based series estimators

• DOI: 10.1214/19-aos1865
• 发表时间: 2018-04
• 期刊: The Annals of Statistics
• 作者: M. D. Cattaneo;M. Farrell;Yingjie Feng

Characteristic-Sorted Portfolios: Estimation and Inference

• DOI: 10.1162/rest_a_00883
• 发表时间: 2018-09
• 期刊: Review of Economics and Statistics
• 影响因子: 8
• 作者: M. D. Cattaneo;Richard K. Crump;M. Farrell;E. Schaumburg

AVERAGE DENSITY ESTIMATORS: EFFICIENCY AND BOOTSTRAP CONSISTENCY

• DOI: 10.1017/s0266466621000530
• 发表时间: 2019-04
• 期刊: Econometric Theory
• 影响因子: 0.8
• 作者: M. D. Cattaneo;Michael Jansson