Some Recent Advances in Measurement Error Models and Methods

Hans Schneeweiß () and Thomas Augustin ()
Additional contact information
Hans Schneeweiß: Ludwig-Maximilians-Universität
Thomas Augustin: Ludwig-Maximilians-Universität

Chapter 13 in Modern Econometric Analysis, 2006, pp 183-198 from Springer

Abstract: Abstract A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these easurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been roposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robustness of these methods have been compared. The aper gives an account of these endeavors. In another context, when data are of a categorical nature, classification errors play a similar role as easurement errors in continuous data. The paper also reviews some recent advances in this field.

Keywords: Consistent Estimator; Partial Likelihood; Measurement Error Model; Regression Calibration; Naive Estimator (search for similar items in EconPapers)
Date: 2006
References: Add references at CitEc
Citations: View citations in EconPapers (2)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-3-540-32693-9_13

Ordering information: This item can be ordered from
http://www.springer.com/9783540326939

DOI: 10.1007/3-540-32693-6_13

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().