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

该奖项将用于研究开发新的统计方法，用于分析经济数据和检验经济理论。其目标是制定新的程序，使其对执行的具体情况更加稳健(即不那么敏感)。该项目推进了科学，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。从可用的数据中得出有效的推论在各种策略设置中也很重要。理想的方法应该是灵活、简单和健壮的，因为结果不会过度依赖于方法在任何给定上下文中应用的具体方式。虽然以前的计量经济学工作已经开发出新的非参数和半参数方法来满足前两个目标，但这些方法并不是很健壮。它们在实践中的使用需要选择在有限样本中的影响未知的选择。因此，许多经验社会科学家使用参数方法。通过开发新的稳健的非参数和半参数方法，PI希望开发出既避免错误说明偏差又具有实用吸引力的方法。他们计划使用另一种渐近理论来推导非参数和半参数统计的新的分布近似。他们计划研究四条线：(I)非线性半参数的替代渐近结果和Bootstrap有效性；(Ii)线性半参数的替代渐近结果和一致标准误差；(Iii)替代渐近结果的高阶展开；(Iv)使用局部多项式技术的新的稳健非参数和半参数方法。

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.