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

David Giles, Hui Feng and Ryan T. Godwin

No 1105, 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 can also result in a small reduction in relative mean squared error. In terms of remaining relative bias, the analytic bias-corrected estimators are somewhat less effective than their counterparts obtained by using a parametric bootstrap bias correction. However, the analytic correction out-performs the bootstrap correction in terms of remaining %MSE. Taking into account the relative computational costs, this leads us to recommend the use of the analytic bias adjustment for most practical situations.

Keywords: Bias reduction; Extreme values; Generalized Pareto distribution; Peaks over threshold; Parametric bootstrap (search for similar items in EconPapers)
JEL-codes: C13 C16 C46 C58 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2011-10-11
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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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 (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:1105

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