Bias - Corrected Maximum Likelihood Estimation of the Parameters of the Generalized Pareto Distribution

David Giles and Hui Feng ()
Additional contact information
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 902, Econometrics Working Papers from Department of Economics, University of Victoria

Abstract: We derive analytic expressions for the biases, to O(n-1) of the maximum likelihood estimators of the parameters of the generalized Pareto distribution. Using these expressions to bias-correct the estimators is found to be extremely effective in terms of bias reduction, and generally results in some reduction in relative mean squared error. The analytic bias-corrected estimators are also shown to be dramatically superior to the alternative of bias-correction via the bootstrap.

Keywords: Bias reduction; Extreme values; Generalized Pareto distribution; Peaks over threshold (search for similar items in EconPapers)
JEL-codes: C13 C16 C41 C46 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2009-01-23
New Economics Papers: this item is included in nep-ecm
Note: ISSN 1485-6441
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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https://www.uvic.ca/socialsciences/economics/_asse ... ometrics/ewp0902.pdf (application/pdf)

Related works:
Journal Article: Bias-corrected maximum likelihood estimation of the parameters of the generalized Pareto distribution (2016)
Working Paper: Bias - Corrected Maximum Likelihood Estimation of the Parameters of the Generalized Pareto Distribution (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:0902

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