Abstract
Maximum likelihood is a method of estimation developed for fully specified parametric likelihood settings. In smooth parametric models, maximum likelihood has a number of desirable properties, including consistency, asymptotic normality, and asymptotic efficiency. Maximum likelihood has been usefully extended to various semiparametric and nonparametric settings.
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
Ai, C. 1997. A semiparametric maximum likelihood estimator. Econometrica 65: 933–963.
Barndorff-Nielsen, O.E. 1983. On a formula for the distribution of the maximum likelihood estimator. Biometrika 70: 343–365.
Berger, J., and R. Wolpert. 1988. The likelihood principle. Hayward: Institute of Mathematical Statistics.
Brown, B., and W. Newey. 2002. Generalized method of moments, efficient bootstrapping, and improved inference. Journal of Business and Economic Statistics 20: 507–517.
Eaton, M. 1989. Group invariance applications in statistics. Hayward: Institute of Mathematical Statistics.
Efron, B. 1982. Maximum likelihood and decision theory. Annals of Statistics 10: 340–356.
Fan, J., M. Farmen, and I. Gijbels. 1998. Local maximum likelihood estimation and inference. Journal of the Royal Statistical Society (Series B) 60: 591–608.
Fisher, R.A. 1922. On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London A 222: 309–360.
Fisher, R.A. 1925. Theory of statistical estimation. Proceedings of the Cambridge Philosophical Society 22: 700–725.
Fisher, R.A. 1934. Two new properties of mathematical likelihood. Proceedings of the Royal Society of London A 144: 285–307.
Gourieroux, C., A. Monfort, and A. Trognon. 1984. Pseudo maximum likelihood methods: theory. Econometrica 52: 681–700.
Hajivassiliou, V., and P. Ruud. 1994. Classical estimation methods for LDV models using simulation. In Handbook of Econometrics, ed. D. McFadden and R. Engle, vol. 4. Amsterdam: North-Holland.
Hirano, K., and J. Porter. 2003. Asymptotic efficiency in parametric structural models with parameter dependent support. Econometrica 71: 1307–1338.
Hirano, K., and J. Porter 2005. Efficiency in asymptotic shift models, Working paper. University of Wisconsin.
Ibragimov, I.A., and R.Z. Has’minskii. 1981. Statistical estimation: Asymptotic theory. New York: Springer-Verlag.
Le Cam, L. 1986. Asymptotic methods in statistical decision theory. New York: Springer-Verlag.
Le Cam, L. 1990. Maximum likelihood: An introduction. International Statistical Review 58: 153–171.
Linton, O., and Z. Xiao. 2007. A nonparametric regression estimator that adapts to error distribution of unknown form. Econometric Theory 23(3) (forthcoming)
MacLachlan, G., and T. Krishnan. 1997. The EM algorithm and extensions. New York: Wiley.
Neyman, J., and E.L. Scott. 1948. Consistent estimates based on partially consistent observations. Econometrica 16: 1–32.
Owen, A.B. 2001. Empirical likelihood. New York: Chapman-Hall.
Pfanzagl, J., and W. Wefelmeyer. 1978. A Third-order optimum property of the maximum likelihood estimator. Journal of Multivariate Analysis 8: 1–29.
Tibshirani, R., and T. Hastie. 1987. Local likelihood estimation. Journal of the American Statistical Association 82: 559–567.
Author information
Authors and Affiliations
Editor information
Copyright information
© 2018 Macmillan Publishers Ltd.
About this entry
Cite this entry
Porter, J.R. (2018). Maximum Likelihood. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_976
Download citation
DOI: https://doi.org/10.1057/978-1-349-95189-5_976
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