The power-law tail exponent of income distributions

F. Clementi; T. Di Matteo; M. Gallegati

doi:10.1016/j.physa.2006.04.027

The power-law tail exponent of income distributions

F. Clementi^*, T. Di Matteo, M. Gallegati

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

45 Citations (Scopus)

Abstract

In this paper we tackle the problem of estimating the power-law tail exponent of income distributions by using the Hill's estimator. A subsample semi-parametric bootstrap procedure minimizing the mean squared error is used to choose the power-law cutoff value optimally. This technique is applied to personal income data for Australia and Italy.

Original language	English
Pages (from-to)	49-53
Number of pages	5
Journal	Physica A: Statistical Mechanics and its Applications
Volume	370
Issue number	1
DOIs	https://doi.org/10.1016/j.physa.2006.04.027
Publication status	Published - 1 Oct 2006

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10.1016/j.physa.2006.04.027

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title = "The power-law tail exponent of income distributions",

abstract = "In this paper we tackle the problem of estimating the power-law tail exponent of income distributions by using the Hill's estimator. A subsample semi-parametric bootstrap procedure minimizing the mean squared error is used to choose the power-law cutoff value optimally. This technique is applied to personal income data for Australia and Italy.",

keywords = "Bootstrap, Hill's estimator, Pareto's index, Personal income",

author = "F. Clementi and \{Di Matteo\}, T. and M. Gallegati",

year = "2006",

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AU - Di Matteo, T.

AU - Gallegati, M.

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N2 - In this paper we tackle the problem of estimating the power-law tail exponent of income distributions by using the Hill's estimator. A subsample semi-parametric bootstrap procedure minimizing the mean squared error is used to choose the power-law cutoff value optimally. This technique is applied to personal income data for Australia and Italy.

AB - In this paper we tackle the problem of estimating the power-law tail exponent of income distributions by using the Hill's estimator. A subsample semi-parametric bootstrap procedure minimizing the mean squared error is used to choose the power-law cutoff value optimally. This technique is applied to personal income data for Australia and Italy.

KW - Bootstrap

KW - Hill's estimator

KW - Pareto's index

KW - Personal income

UR - https://www.scopus.com/pages/publications/33747589434

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DO - 10.1016/j.physa.2006.04.027

M3 - Article

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JO - Physica A: Statistical Mechanics and its Applications

JF - Physica A: Statistical Mechanics and its Applications

IS - 1

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The power-law tail exponent of income distributions

Abstract

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