Point and Set Identification in Linear Panel Data Models with Measurement Error

13 Pages Posted: 26 May 2012

See all articles by Erik Meijer

Erik Meijer

University of Southern California; RAND Corporation

Laura Spierdijk

University of Twente - Department of Behavioural, Management and Social Sciences - Financial Engineering section

Tom Wansbeek

University of Groningen - Faculty of Economics and Business

Date Written: March 9, 2012

Abstract

The rich dependency structure of panel data can be exploited to generate moment conditions that can be used to identify linear regression models in the presence of measurement error. This paper adds to a small body of literature on this topic by showing how heteroskedasticity and nonlinear relationships between the error-ridden regressors and error-free regressors lead to identifying moment conditions in a static panel data setting, how suitably chosen linear combinations of lagged and lead values of the dependent variable can be used as instrumental variables in a dynamic panel data with measurement errors, and how reverse regression can be generalized to the panel data setting, thereby giving bounds on regression coefficients in the absence of point identification.

JEL Classification: C23, C26

Suggested Citation

Meijer, Erik and Spierdijk, Laura and Wansbeek, Tom, Point and Set Identification in Linear Panel Data Models with Measurement Error (March 9, 2012). RAND Working Paper Series WR- 941, Available at SSRN: https://ssrn.com/abstract=2066869 or http://dx.doi.org/10.2139/ssrn.2066869

Erik Meijer (Contact Author)

University of Southern California ( email )

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RAND Corporation ( email )

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Laura Spierdijk

University of Twente - Department of Behavioural, Management and Social Sciences - Financial Engineering section ( email )

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Tom Wansbeek

University of Groningen - Faculty of Economics and Business ( email )

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9700 AB Groningen
Netherlands

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