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, 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 heteroskedastic 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.
|Date of creation:||19 Dec 2003|
|Date of revision:||15 Dec 2010|
|Publication status:||published, Journal of Business and Economic Statistics, 2012, 30, 67-80|
|Note:||Previously circulated as "Identification of Heteroskedastic Endogenous or Mismeasured Regressor Models."|
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