Large Deviation Approach for Moment Condition Models

本计画应用大离差理论来评估矩条件模型的计量方法。大离差理论使我们能够探索计量经济学方法的一阶但全局渐近性质，并提供了与传统计量经济学理论主要关注局部渐近性质不同的观点。该项目引入了大偏差方法来解决矩条件模型的三个决策问题：参数估计和检验，集合推理，和时刻选择。该项目的主要目的是当现有方法在传统计量经济学理论下难以区分时，寻找大偏差最优计量经济学方法（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（集合推理和矩选择问题）。作为一种更广泛的影响，将这些最佳方法应用于实证经济分析，如对教育回报和收入动态的分析，通过提供政策研究的一些新见解，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.