Abstract
Bias correction is a statistical technique used to remove the bias of an estimator. An unbiased estimator is such that its expectation is equal to the parameter of interest. Many introductory statistics textbooks discuss the desirability of having an unbiased estimator, although it is quickly pointed out that unbiasedness alone cannot be a good criterion for an estimator. This is usually illustrated by comparing two estimators with the use of a concrete loss function, where it is noted that an unbiased estimator with a large variance may be inferior to a biased estimator with a small variance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
Arellano, M. 2003. Discrete choices with panel data. Investigaciones Económicas 27: 423–458.
Bekker, P. 1994. Alternative approximations to the distributions of instrumental variable estimators. Econometrica 62: 657–681.
Hahn, J., and W. Newey. 2004. Jackknife and analytical bias reduction for nonlinear panel models. Econometrica 72: 1295–1319.
Newey, W., and R. Smith. 2004. Higher order properties of GMM and generalized empirical likelihood estimators. Econometrica 72: 219–255.
Neyman, J., and E. Scott. 1948. Consistent estimates based on partially consistent observations. Econometrica 16: 1–31.
Pfanzagl, J., and W. Wefelmeyer. 1978. A third-order optimum property of the maximum likelihood estimator. Journal of Multivariate Analysis 8: 1–29.
Woutersen, T. 2002. Robustness against incidental parameters. Working Paper No. 20028. Department of Economics/University of Western Ontario.
Author information
Authors and Affiliations
Editor information
Copyright information
© 2018 Macmillan Publishers Ltd.
About this entry
Cite this entry
Hahn, J. (2018). Bias Correction. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2020
Download citation
DOI: https://doi.org/10.1057/978-1-349-95189-5_2020
Published:
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-95188-8
Online ISBN: 978-1-349-95189-5
eBook Packages: Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences