IDEAS home Printed from https://ideas.repec.org/p/arx/papers/physics-0603061.html
   My bibliography  Save this paper

The Power-law Tail Exponent of Income Distributions

Author

Listed:
  • F. Clementi
  • T. Di Matteo
  • M. Gallegati

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 minimising 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.

Suggested Citation

  • F. Clementi & T. Di Matteo & M. Gallegati, 2006. "The Power-law Tail Exponent of Income Distributions," Papers physics/0603061, arXiv.org.
    • Handle: RePEc:arx:papers:physics/0603061
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/physics/0603061
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Olivier V. Pictet & Michel M. Dacorogna & Ulrich A. Muller, 1996. "Hill, Bootstrap and Jackknife Estimators for Heavy Tails," Working Papers 1996-12-10, Olsen and Associates.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lautier, Delphine & Raynaud, Franck, 2011. "Statistical properties of derivatives: A journey in term structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2009-2019.
    2. Guo, Qiang & Gao, Li, 2012. "Distribution of individual incomes in China between 1992 and 2009," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5139-5145.
    3. Hu, Feng-Rung, 2008. "On the estimation of the power-law exponent in the mean-field Bouchaud–Mézard model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(18), pages 4605-4614.
    4. C. Guilmi & F. Clementi & T. Matteo & M. Gallegati, 2008. "Social networks and labour productivity in Europe: an empirical investigation," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 3(1), pages 43-57, June.
    5. Brzezinski, Michal, 2014. "Do wealth distributions follow power laws? Evidence from ‘rich lists’," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 155-162.
    6. Costas Efthimiou & Adam Wearne, 2016. "Household Income Distribution in the USA," Papers 1602.06234, arXiv.org.
    7. Coronel-Brizio, H.F. & Hernández-Montoya, A.R., 2010. "The Anderson–Darling test of fit for the power-law distribution from left-censored samples," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3508-3515.
    8. Chiarella Carl & Di Guilmi Corrado, 2015. "The limit distribution of evolving strategies in financial markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 137-159, April.
    9. Laura Carvalho & Corrado Di Guilmi, 2020. "Technological unemployment and income inequality: a stock-flow consistent agent-based approach," Journal of Evolutionary Economics, Springer, vol. 30(1), pages 39-73, January.
    10. Liu, Shengli & Liang, Yongtu, 2021. "Statistics of catastrophic hazardous liquid pipeline accidents," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    11. Boris M. Dolgonosov, 2018. "A Conceptual Model of the Relationship Among World Economy and Climate Indicators," Biophysical Economics and Resource Quality, Springer, vol. 3(1), pages 1-15, March.
    12. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.
    13. Alberto Russo, 2009. "On the evolution of the Italian bank branch distribution," Economics Bulletin, AccessEcon, vol. 29(3), pages 2063-2078.
    14. Zoltan Kuscsik & Denis Horvath, 2007. "Statistical properties of agent-based market area model," Papers 0710.0459, arXiv.org.
    15. Ermanno Catullo & Antonio Palestrini & Ruggero Grilli & Mauro Gallegati, 2018. "Early warning indicators and macro-prudential policies: a credit network agent based model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 81-115, April.
    16. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman, 2018. "Optimal threshold for Pareto tail modelling in the presence of outliers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 169-180.
    17. Rota, Mauro & Schettino, Francesco & Spinesi, Luca, 2017. "Key inventors, teams and firm performance: The Italian case," Structural Change and Economic Dynamics, Elsevier, vol. 42(C), pages 13-25.
    18. Victor M. Yakovenko & J. Barkley Rosser, 2009. "Colloquium: Statistical mechanics of money, wealth, and income," Papers 0905.1518, arXiv.org, revised Dec 2009.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    2. Wolfgang Breymann & Leah Kelly & Eckhard Platen, 2005. "Intraday Empirical Analysis and Modeling of Diversified World Stock Indices," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 12(1), pages 1-28, March.
    3. repec:hal:journl:hal-00880258 is not listed on IDEAS
    4. Weshah Razzak, "undated". "On the GCC Currency Union," API-Working Paper Series 0910, Arab Planning Institute - Kuwait, Information Center.
    5. Raymond Knott & Marco Polenghi, 2006. "Assessing central counterparty margin coverage on futures contracts using GARCH models," Bank of England working papers 287, Bank of England.
    6. Ibrahim Onour, "undated". "Extreme Risk and Fat-tails Distribution Model:Empirical Analysis," API-Working Paper Series 0911, Arab Planning Institute - Kuwait, Information Center.
    7. Kratz , Marie, 2013. "There is a VaR Beyond Usual Approximations," ESSEC Working Papers WP1317, ESSEC Research Center, ESSEC Business School.
    8. Dr. Ibrahim Onour, "undated". "The Global Financial Crisis and Equity Markets in Middle East Oil Exporting Countries," API-Working Paper Series 1009, Arab Planning Institute - Kuwait, Information Center.
    9. Marie Kratz, 2013. "There is a VaR beyond usual approximations," Papers 1311.0270, arXiv.org.
    10. David Edelman & Thomas Gillespie, 2000. "The Stochastically Subordinated Poisson Normal Process for Modelling Financial Assets," Annals of Operations Research, Springer, vol. 100(1), pages 133-164, December.
    11. Tsourti, Zoi & Panaretos, John, 2003. "Extreme Value Index Estimators and Smoothing Alternatives: A Critical Review," MPRA Paper 6390, University Library of Munich, Germany.
    12. Michel Dacorogna & Peter Blum, 2003. "Extreme Moves in Foreign Exchange Rates and Risk Limit Setting," Risk and Insurance 0306004, University Library of Munich, Germany.
    13. Céline Azizieh & Wolfgang Breymann, 2005. "Estimation of the Stylized Facts of a Stochastic Cascade Model," Working Papers CEB 05-009.RS, ULB -- Universite Libre de Bruxelles.
    14. Sayantan Ghosh & P. Manimaran & Prasanta K. Panigrahi, 2010. "Characterizing Multi-Scale Self-Similar Behavior and Non-Statistical Properties of Financial Time Series," Papers 1003.2539, arXiv.org, revised Dec 2010.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:physics/0603061. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.