AI 中文总结
研究非线性面板数据模型在观测数少的情况下的附带参数问题，核心方法是通过投影消除附带参数，主要贡献是得到部分可识别的不完整模型，利用矩不等式刻画识别集，并给出相关应用示例。
AI 中文摘要
本文介绍了一种新方法，用于在每个观测单位的观测数量较少时，对非线性面板数据模型进行计量经济分析。在这类模型中，单位内恒定但单位间变化的变量会导致附带参数问题。本文采用的方法通过投影消除这些附带参数，生成一个对应关系，指定通过选择单位特定附带参数的某些值可实现的所有观测变量和单位内变化的未观测异质性的组合。去除单位特定变量后，无需对其与其他变量的联合分布做假设。结果是一个通常部分可识别的不完整模型。利用随机集理论工具通过矩不等式来刻画识别集。给出了使用对单位内变化的未观测异质性的无分布限制应用于具有离散或连续结果的静态和动态模型的示例。
英文摘要
This paper introduces a new approach to econometric analysis of nonlinear panel data models when the number of observations per observational unit is small. In such models the presence of variables that are constant within, while varying across, units results in an incidental parameter problem. The approach taken in this paper removes these incidental parameters via projection, which produces a correspondence specifying all combinations of observed variables and within-unit-varying unobserved heterogeneity that are achievable by choice of some value of the unit-specific incidental parameters. With unit-specific variables removed, there is no need for assumptions concerning their joint distribution with other variables. The result is an incomplete model which is typically partially identifying. Identified sets are characterized via moment inequalities using tools of random set theory. Examples of application to static and dynamic models with discrete or continuous outcomes using distribution-free restrictions on within-unit-varying unobserved heterogeneity are presented.
