Bias of the Maximum Likelihood Estimators of the Two-Parameter Gamma Distribution Revisited

David Giles and Hui Feng ()
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Hui Feng: Department of Economics, Business & Mathematics, King's College, University of Western Ontario, https://www.kings.uwo.ca/academics/school-of-management-economics-and-mathematics/economics-and-finance/

No 908, Econometrics Working Papers from Department of Economics, University of Victoria

Abstract: We consider the quality of the maximum likelihood estimators for the parameters of the two-parameter gamma distribution in small samples. We show that the methodology suggested by Cox and Snell (1968) can be used very easily to bias-adjust these estimators. A simulation study shows that this analytic correction is frequently much more effective than bias-adjusting using the bootstrap – generally by an order of magnitude in percentage terms. The two bias-correction methods considered result in increased variability in small samples, and the original estimators and their bias-corrected counterparts all have similar percentage mean squared errors.

Keywords: Maximum likelihood estimator; bias reduction; gamma distribution (search for similar items in EconPapers)
JEL-codes: C13 C46 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2009-09-23
New Economics Papers: this item is included in nep-ecm
Note: ISSN 1485-6441
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:0908

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