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Some Recent Advances in Measurement Error Models and Methods

In: Modern Econometric Analysis

Author

Listed:
  • Hans Schneeweiß

    (Ludwig-Maximilians-Universität)

  • Thomas Augustin

    (Ludwig-Maximilians-Universität)

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.

Suggested Citation

  • Hans Schneeweiß & Thomas Augustin, 2006. "Some Recent Advances in Measurement Error Models and Methods," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 13, pages 183-198, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-32693-9_13
    DOI: 10.1007/3-540-32693-6_13
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    Cited by:

    1. Angela Blanco-Fernández & Peter Winker, 2016. "Data generation processes and statistical management of interval data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(4), pages 475-494, October.
    2. Thomas Augustin & Helmut Küchenhoff & Matthias Schmid, 2022. "Nachruf Hans Schneeweiß," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(2), pages 149-154, June.

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