Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models
提出一种利用异方差协方差约束来识别测量误差和内生性模型的新方法，可在缺乏工具变量时使用，并提供了半参数部分线性模型的估计和集合识别边界。
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.