Measurement Error Models with Auxiliary Data
带辅助数据的测量误差模型AI 解读 ↓
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带辅助数据的测量误差模型
- 研究问题
- 当真实变量与测量误差可以任意相关时，如何借助辅助数据识别并估计可能非线性、非光滑的计量模型参数？
- 方法与思路
- 作者要求真实变量给定误测变量的条件分布在两套数据中相同，构造筛估计并合并数据，允许验证样本或分层辅助样本。
- 材料陈述的贡献
- 摘要称，方法允许无界支持而无需通常的截尾处理，建立大样本性质，并展示数据合并提高效率及删失分位数回归应用。
- 阅读关注点
- 应核对跨数据条件分布相同的假设、分层抽样如何处理、筛估计的正则条件，以及教育回报应用中的误测校正。
- 阅读边界
- 摘要未给出筛空间、具体收敛条件或效率增益数值；允许任意相关仍依赖所述条件分布可迁移假设。
原文摘要
We study the problem of parameter inference in (possibly non-linear and non-smooth) econometric models when the data are measured with error. We allow for arbitrary correlation between the true variables and the measurement errors. To solve the identification problem, we require the existence of an auxiliary data-set that contains information about the conditional distribution of the true variables given the mismeasured variables. Our main assumption requires that the conditional distribution of the true variables given the mismeasured variables is the same in the primary and auxiliary data. Our methods allow the auxiliary data to be a validation sample, where the primary and validation data are from the same distribution, and more importantly, a stratified sample where the auxiliary data-set is not from the same distribution as the primary data. We also show how to combine the two data-sets to obtain a more efficient estimator of the parameter of interest. We establish the large sample properties of the sieve based estimators under verifiable conditions. In particular, we allow for the mismeasured variables to have unbounded supports without employing the tedious trimming scheme typically used in kernel based methods. We illustrate our methods by estimating a returns to schooling censored quantile regression using the CPS/SSR 1978 exact match files where the dependent variable is measured with error of arbitrary kind. Copyright 2005, Wiley-Blackwell.