Inference Under Moment Restrictions

在这个项目中，研究人员将分析组合不同数据集的技术。这项研究的一般目的是建立在不同数据集的独特优势之上。一些数据集，如当前人口调查（CPS）特别具有代表性，但可能只包含有限的变量。其他数据集，如国家纵向调查（NLS）或收入动态小组研究（PSID）可能不如CPS具有代表性，但具有更详细信息的优势。此外，像NLS和PSID这样的数据集有同一单位在较长时间内的信息（面板数据），而不是只有一次（横截面）。将开发方法，扩展以前在计量经济学和统计学中开发的方法，以便将这些数据集结合起来。它们将与标准方法（如加权）相连接，并在缺失数据问题的上下文中进行解释。这项研究将有助于更好地理解计量经济学中使用的技术（如广义矩估计方法）与统计技术（如耙法和加权法）之间的联系。然后将这些方法应用于人工和真实数据集，以评估它们在现实环境中的性能。

In this project, the researcher will analyze techniques for combining different datasets. The general purpose of this research is to build on the distinct strengths of the different datasets. Some datasets such as the Current Population Survey (CPS) are particularly representative but may contain only limited variables. Other datasets, such as the National Longitudinal Survey (NLS) or the Panel Study of Income Dynamics (PSID) may be less representative than the CPS but have the advantage of more detailed information. In addition, datasets such as the NLS and PSID have information on the same units over longer periods of time (panel data) rather than only once (cross-sections). Methods will be developed, extending those previously developed in econometrics and statistics, that allow combining such datasets. They will be connected to standard methods such as weighting and interpreted in the context of missing data problems. This research will lead to a better understanding of the links between techniques used in econometrics, such as generalized method of moments estimation, and techniques in statistics such as raking and weighting. These methods will then be applied to artificial as well as real datasets to evaluate their performance in a realistic setting.