New Econometric Methods for Estimation and Inferences in Nonlinear Econometric Models

该项目在三个主题中开发了新的工具和见解，这将对经验微观经济学家和社会科学家有用。第一个问题涉及样本选择问题，其中数据不能代表感兴趣的总体。不处理这个问题可能会导致错误的结论。研究人员提供了一个更好的理解这个问题，并制定方法，以避免强有力的假设，通常是由传统的方法。第二个主题涉及包含大量个人或公司的面板数据，这些个人或公司的时间段超过一个。研究人员通过询问是否有可能准确地学习感兴趣的对象，即使有无限数量的数据，来检查现有面板数据非线性模型的更一般版本。如果答案是否定的，这个项目进一步构造感兴趣的参数的界限。第三个主题是研究人员如何计算与数据集分析相关的统计不确定性。研究人员开发的工具，可以使用时，现有的工具是计算上不可行的，而不是从理论的角度来看，更好的工具。本研究开发的工具可用于经济学和社会科学的许多领域。为了促进这一点，研究人员还制作了计算机程序，供其他研究人员在自己的工作中使用。这项研究开发了新的工具和见解，将是有益的经验微观经济学家。该项目有三个部分。第一个主题是样本选择模型。这些模型在经济学中有着悠久的历史，在许多领域都有应用。为了估计样本选择模型，以前的研究通常假设排除限制，因为一些变量影响样本的选择，但对感兴趣的结果没有影响。该项目提供了一个更好地了解这个问题，并制定方法，以减轻它。研究人员专注于模型的兴趣变量是二进制和调查的程度，它是可能的，以估计这些模型的参数。最后，本研究为研究人员开发了更容易进行统计推断的工具。具体来说，它开发了一个简单版本的所谓的“引导”，这可以计算更方便的复杂模型比替代程序。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This project develops new tools and insights in three topics that will be useful to empirical microeconomists and social scientists. The first concerns the sample selection problem in which data are not representative of the population of interest. Not dealing with this problem can lead to incorrect conclusions. The investigator provides a better understanding of this problem and develops methods to avoid strong assumptions that are typically imposed by traditional approaches. The second topic is related to panel data that contain a large number of individuals or firms with more than one-time period. The investigator examines more general versions of existing nonlinear models for panel data by asking whether it is possible to exactly learn the object of interest even with infinite amounts of data. If the answer is negative, this project further constructs bounds for the parameters of interest. The third topic is concerned with how researchers calculate the statistical uncertainty associated with the analysis of a data set. The investigator develops tools that can be used when existing tools are computationally infeasible, as opposed to tools that are better from a theoretical point of view. The tools developed in this research can be used in many areas of economics and social sciences. To facilitate this, the investigator also produces computer programs for other researchers to utilize in their own work. This research develops new tools and insights that will be useful to empirical microeconomists. The project has three parts. The first topic is sample selection models. These models have a long history in economics with applications in many areas. To estimate sample selection models, previous studies often assumed exclusion restrictions, in that some variables influence the selection into the sample, but do not have an effect on the outcome of interest. This project provides a better understanding of this issue and develops methods to alleviate it. The second part of the project investigates panel data. The investigator focuses on models where the variable of interest is binary and investigates the extent to which it is possible to estimate the parameters of such models. Finally, this research develops tools for researchers to more easily make statistical inference. Specifically, it develops a simpler version of the so-called "bootstrap", which can be computationally more convenient for complicated models than alternative procedures.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.