Using Heteroskedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models
This paper proposes a new method of obtaining identification in mismeasured regressor models, triangular systems, linear simultaneous equation systems, and structural vector autoregressions. Associated estimators take the form of ordinary two stage least squares or generalized method of moments. The method may be used in applications where other sources of identification such as instrumental variables or repeated measurements are not available. Identification comes from a heteroskedastic covariance restriction that is shown to be a feature of many models of endogeneity and of measurement errors. Identification is also obtained in some semiparametric partly linear models. Set identification bounds are provided for cases where point identifying assumptions fail to hold. An empirical application and a Monte Carlo study are provided.
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