Abstract
The maximum likelihood estimation principle, unbiasedness and hypothesis testing serve as foundation stones for much that goes on in the lives of theoretical and applied econometricians. In this context, the purpose of these words and other symbols is to review the statistical implications of pursuing these estimation and inference goals and to suggest superior alternatives.
This chapter was originally published in The New Palgrave Dictionary of Economics, 2nd edition, 2008. Edited by Steven N. Durlauf and Lawrence E. Blume
This is a preview of subscription content, log in via an institution.
Bibliography
Baranchik, A. 1964. Multiple regression and estimation of the mean of multivariate normal distribution, Technical report 51. Stanford: Stanford University, Department of Statistics.
Bock, M., T. Yancey, and G. Judge. 1973. The statistical consequences of preliminary test estimators in regression. Journal of American Statistical Association 68: 107–110.
Efron, B., and C. Morris. 1973. Combining possibly related estimation problems. Journal of the Royal Statistical Society, Series B 35: 379–421.
Green, E., and W. Strawderman. 1991. James-Stein-type estimator for combining unbiased and possibly biased estimators. Journal of the American Statistical Associations 86: 1001–1006.
James, W., and C. Stein. 1961. Estimation with quadratic loss. In Proceedings of the fourth Berkley symposium on mathematical statistics and probability, vol. 1. Berkeley/Los Angeles: University of California Press.
Judge, G., and M. Bock. 1978. The statistical implications of pretest and Stein-Rule estimators in econometrics. New York: North-Holland.
Judge, G., and M. Bock. 1983. Biased estimation. In Handbook of econometrics, vol. 1, ed. Z. Griliches and M. Intriligator. Amsterdam: North-Holland.
Judge, G., and R. Mittelhammer. 2004. A semiparametric basis for combining estimation problems under quadratic loss. Journal of American Statistical Association 49: 479–487.
Judge, G., R. Hill, W. Griffiths, H. Lutkepohl and T. Lee. 1988. Introduction to the theory and practice of econometrics, ch. 20. New York: Wiley.
Lindley, D. 1962. Discussion of professor Stein’s paper. Journal of the Royal Statistical Society, Series B 24: 285–288.
Mittelhammer, R., G. Judge, D. Miller, and S. Cardell. 2005. Minimum divergence moment based binary response models: Estimation and inference, Working paper No. 998. Berkeley: CUDARE, University of California, Berkeley.
Sclove, S., C. Morris, and R. Radhakrishman. 1972. Non-optimality of preliminary-test estimators for the multinormal mean. Annals of Mathematical Statistics 43: 1481–1490.
Stein, C. 1955. Inadmissibility of the usual estimator for the mean of a multivariate normal distribution. In Proceedings of the third Berkeley symposium on mathematical statistics and probability, vol. 1. Berkeley/Los Angeles: University of California Press.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Copyright information
© 2008 The Author(s)
About this entry
Cite this entry
Judge, G.G. (2008). Shrinkage-Biased Estimation in Econometrics. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95121-5_1923-1
Download citation
DOI: https://doi.org/10.1057/978-1-349-95121-5_1923-1
Received:
Accepted:
Published:
Publisher Name: Palgrave Macmillan, London
Online ISBN: 978-1-349-95121-5
eBook Packages: Springer Reference Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences