Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models
Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models
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DOI：
10.1080/07350015.2012.643126
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发表时间：
2012-01-01
影响因子：
3
通讯作者：
Lewbel, Arthur
中科院分区：
文献类型：
--
作者：
Lewbel, Arthur
本文提出了一种新的方法来获得辨识错测回归模型，三角系统和联立方程组。该方法可用于无法获得其他识别来源（如仪器变量或重复测量）的应用中。相关估计量采用两阶段最小二乘或广义矩量法的形式。识别来自异方差协方差限制，这是许多内生性或测量错误模型的特征。给出了半参数部分线性模型的辨识方法，并给出了相关估计量。对于点识别假设不成立的情况，导出了集合识别边界。给出了一个估计恩格尔曲线的经验应用。
This article proposes a new method of obtaining identification in mismeasured regressor models, triangular systems, and simultaneous equation systems. The method may be used in applications where other sources of identification, such as instrumental variables or repeated measurements, are not available. Associated estimators take the form of two-stage least squares or generalized method of moments. Identification comes from a heteroscedastic covariance restriction that is shown to be a feature of many models of endogeneity or mismeasurement. Identification is also obtained for semiparametric partly linear models, and associated estimators are provided. Set identification bounds are derived for cases where point-identifying assumptions fail to hold. An empirical application estimating Engel curves is provided.