Imposing moment restrictions from auxiliary data by weighting
Imposing moment restrictions from auxiliary data by weighting
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DOI：
10.1162/003465399557860
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发表时间：
1999-02-01
影响因子：
8
通讯作者：
Imbens, GW
中科院分区：
文献类型：
--
作者：
Hellerstein, JK;Imbens, GW
本文分析了矩约束下回归模型系数的估计，其中矩约束是由辅助数据导出的。矩限制为每个观测值生成权重，这些权重随后可用于加权回归分析。我们在两个假设下讨论了这些权重的解释：目标人口（构成矩）和抽样人口（抽取样本）是相同的，这些人口不同。我们提出了一个基于省略能力偏差的工资回归估计的应用。国家纵向调查青年男子队列（NLS）-除了包含关于工资，教育和经验的每一个观察的信息-记录了两个测试分数的数据，可以被认为是能力的代理。然而，NLS是一个小数据集，具有高损耗率。我们研究如何减轻这些问题，在NLS形成的时刻，教育，经验和日志工资在1980年美国人口普查的1%样本的联合分布，并使用这些时刻构建权重的加权回归分析的NLS。我们分析了我们的加权回归技术的估计系数和标准误差的回报，教育和经验的NLS控制能力的影响，有和没有假设NLS和人口普查样本是随机样本来自同一人口。
In this paper we analyze the estimation of coefficients in regression models under moment restrictions in which the moment restrictions are derived from auxiliary data. The moment restrictions yield weights for each observation that can subsequently be used in weighted regression analysis. We discuss the interpretation of these weights under two assumptions: that the target population (from which the moments are constructed) and the sampled population (from which the sample is drawn) are the same, and that these populations differ. We present an application based on omitted ability bias in estimation of wage regressions. The National Longitudinal Survey Young Men's Cohort (NLS)- in addition to containing information for each observation on wages, education, and experience - records data on two test scores that may be considered proxies for ability. The NLS is a small dataset, however, with a high attrition rate. We investigate how to mitigate these problems in the NLS by forming moments from the joint distribution of education, experience, and log wages in the 1% sample of the 1980 U.S. Census and using these moments to construct weights for weighted regression analysis of the NLS. We analyze the impacts of our weighted regression techniques on the estimated coefficients and standard errors of returns to education and experience in the NLS controlling for ability, with and without the assumption that the NLS and the Census samples are random samples from the same population.