Large Deviation Approach for Moment Condition Models

本项目应用大偏差理论对矩态模型的计量方法进行评价。大偏差理论使我们能够探索计量经济学方法的一阶全局渐近性质，并提供了一个不同于传统计量经济学理论主要关注局部渐近性质的观点。介绍了矩条件模型参数估计与测试、集推理和矩选择三个决策问题的大偏差方法。本项目的主要目的是在现有方法在常规计量经济学理论下难以区分（参数估计和检验问题）或没有可用的框架来评估计量经济学方法（集推理和矩选择问题）时，寻找大偏差的最优计量经济学方法。从更广泛的影响来看，将这些最优方法应用于实证经济分析，例如对教育回报和收入动态的分析，通过为政策研究提供一些新的见解，对社会有益。

This project applies large deviation theory to evaluate econometric methods for moment condition models. Large deviation theory enables us to explore the first-order but global asymptotic properties of econometric methods and provides a different viewpoint from the conventional econometric theory, which mainly focuses on local asymptotic properties. The project introduces the large deviation approach to three decision problems for moment condition models: parameter estimation and testing, set inference, and moment selection.The main purpose of this project is to find large deviation optimal econometric methods when existing methods are indistinguishable under the conventional econometric theory (parameter estimation and testing problems) or there is no available framework to evaluate econometric methods (set inference, and moment selection problems). As a broader impact, applications of those optimal methods to empirical economic analysis, such as analysis on returns to schooling and income dynamics, are beneficial for society by providing some new insights on policy studies.