Inference Under Moment Restrictions

力矩限制下的推理

• 批准号: 9511718
• 负责人: Guido Imbens
• 金额: $ 3.54万
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-08-15 至 1997-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9511718&HistoricalAwards=false
• 关键词: Inference; Under; Moment; Restrictions

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.

在这个项目中，研究人员将分析组合不同数据集的技术。这项研究的总体目的是建立在不同数据集的不同优势之上。一些数据集，如当前人口普查(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)可能没有CPS那么有代表性，但具有更详细的信息的优势。此外，NLS和PSID等数据集具有更长时间段内相同单位的信息(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，允许合并此类数据集。它们将与加权等标准方法相联系，并在丢失数据问题的情况下进行解释。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，与统计学中的技术，如RAG和加权之间的联系。然后将这些方法应用于人工数据集以及真实数据集，以评估它们在真实环境中的性能。