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

Erik Meijer, Laura Spierdijk and Tom Wansbeek

No WR-941, Working Papers from RAND Corporation

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-codes: C23 C26 (search for similar items in EconPapers)
Pages: 13
Date: 2012-06
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.rand.org/content/dam/rand/pubs/working_papers/2012/RAND_WR941.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ran:wpaper:wr-941

Access Statistics for this paper

More papers in Working Papers from RAND Corporation Contact information at EDIRC.
Bibliographic data for series maintained by Benson Wong ().