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.
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This work was supported by the Deutsche Forschungsgemeinschaft (DFG) within the frame of the Sonderforschungsbereich SFB 386. We thank two anonymous eferees for their helpful comments.
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Schneeweiß, H., Augustin, T. (2006). Some Recent Advances in Measurement Error Models and Methods. In: Hübler, O., Frohn, J. (eds) Modern Econometric Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32693-6_13
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DOI: https://doi.org/10.1007/3-540-32693-6_13
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