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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Length: 32 pages Date of creation: 19 Dec 2003 Date of revision:
27 Nov 2007 Handle: RePEc:boc:bocoec:587
Note: Previously circulated as "Identification of Heteroskedastic Endogenous or Mismeasured Regressor Models." Contact details of provider: Postal: Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA Phone: 617-552-3670 Fax: +1-617-552-2308 Email: Web page: http://fmwww.bc.edu/EC/ More information through EDIRC
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Find related papers by JEL classification: C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
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