Using Heteroskedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models

No 587, Boston College Working Papers in Economics from Boston College Department of Economics

Abstract: 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.

Keywords: simultaneous; endogenous; identification; heteroscedasticity; measurement error; partly linear models (search for similar items in EconPapers)
JEL-codes: C13 C14 C3 D12 (search for similar items in EconPapers)
Date: 2003-12-19, Revised 2010-12-15
Note: Previously circulated as "Identification of Heteroskedastic Endogenous or Mismeasured Regressor Models."
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Citations: View citations in EconPapers (24)

Published, Journal of Business and Economic Statistics, 2012, 30, 67-80

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Related works:
Journal Article: Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models (2012)
Journal Article: Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:bocoec:587

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