IDEAS home Printed from https://ideas.repec.org/a/bla/revinw/v59y2013i2p360-374.html

Skewness And Kurtosis Properties Of Income Distribution Models

Author

Listed:
  • JAMES B. MCDONALD
  • JEFF SORENSEN
  • PATRICK A. TURLEY

Abstract

This paper explores the ability of some popular income distributions to model observed skewness and kurtosis. We present the generalized beta type 1 (GB1) and type 2 (GB2) distributions’ skewness-kurtosis spaces and clarify and expand on previously known results on other distributions’ skewness-kurtosis spaces. Data from the Luxembourg Income Study are used to estimate sample moments and explore the ability of the generalized gamma, Dagum, Singh-Maddala, beta of the first kind, beta of the second kind, GB1, and GB2 distributions to accommodate the skewness and kurtosis values. The GB2 has the flexibility to accurately describe the observed skewness and kurtosis.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • James B. Mcdonald & Jeff Sorensen & Patrick A. Turley, 2013. "Skewness And Kurtosis Properties Of Income Distribution Models," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 59(2), pages 360-374, June.
    • Handle: RePEc:bla:revinw:v:59:y:2013:i:2:p:360-374
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/roiw.2013.59.issue-2
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Karagiorgis, Ariston & Drakos, Konstantinos, 2022. "The Skewness-Kurtosis plane for non-Gaussian systems: The case of hedge fund returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    2. Andrew M. Jones & James Lomas & Nigel Rice, 2015. "Healthcare Cost Regressions: Going Beyond the Mean to Estimate the Full Distribution," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1192-1212, September.
    3. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & AL-Dhurafi, Nasr Ahmed, 2020. "The power-law distribution for the income of poor households," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    4. Michael McAleer & Hang K. Ryu & Daniel J. Slottje, 2019. "A New Inequality Measure that is Sensitive to Extreme Values and Asymmetries," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(1), pages 31-61, March.
    5. David Mauler & James McDonald, 2015. "Option Pricing and Distribution Characteristics," Computational Economics, Springer;Society for Computational Economics, vol. 45(4), pages 579-595, April.
    6. Marco Bee & Julien Hambuckers & Flavio Santi & Luca Trapin, 2021. "Testing a parameter restriction on the boundary for the g-and-h distribution: a simulated approach," Computational Statistics, Springer, vol. 36(3), pages 2177-2200, September.
    7. Jones, A. & Lomas, J. & Rice, N., 2014. "Going Beyond the Mean in Healthcare Cost Regressions: a Comparison of Methods for Estimating the Full Conditional Distribution," Health, Econometrics and Data Group (HEDG) Working Papers 14/26, HEDG, c/o Department of Economics, University of York.
    8. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    9. Enrico Fabrizi & Maria Rosaria Ferrante & Carlo Trivisano, 2020. "A functional approach to small area estimation of the relative median poverty gap," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1273-1291, June.
    10. Andrew M. Jones & James Lomas & Peter T. Moore & Nigel Rice, 2016. "A quasi-Monte-Carlo comparison of parametric and semiparametric regression methods for heavy-tailed and non-normal data: an application to healthcare costs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 951-974, October.
    11. Duangkamon Chotikapanich & William E. Griffiths & Gholamreza Hajargasht & Wasana Karunarathne & D. S. Prasada Rao, 2018. "Using the GB2 Income Distribution," Econometrics, MDPI, vol. 6(2), pages 1-24, April.
    12. Wang, Frank Xuyan, 2021. "Shape factor asymptotic analysis II," MPRA Paper 110827, University Library of Munich, Germany.
    13. Wang, Frank Xuyan, 2019. "Shape Factor Asymptotic Analysis I," MPRA Paper 93357, University Library of Munich, Germany.
    14. Sriubaite, I. & Harris, A. & Jones, A.M. & Gabbe, B., 2020. "Economic Consequences of Road Traffic Injuries. Application of the Super Learner algorithm," Health, Econometrics and Data Group (HEDG) Working Papers 20/20, HEDG, c/o Department of Economics, University of York.
    15. McDonald, James B. & Sorensen, Jeff, 2017. "Academic salary compression across disciplines and over time," Economics of Education Review, Elsevier, vol. 59(C), pages 87-104.
    16. Ariston Karagiorgis & Antonis Ballis & Konstantinos Drakos, 2024. "The Skewness‐Kurtosis plane for cryptocurrencies' universe," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2543-2555, April.
    17. Jingjing Bai & Wei Gu & Xiaodong Yuan & Qun Li & Feng Xue & Xuchong Wang, 2015. "Power Quality Prediction, Early Warning, and Control for Points of Common Coupling with Wind Farms," Energies, MDPI, vol. 8(9), pages 1-18, August.
    18. Tomson Ogwang, 2022. "The Foster–Greer–Thorbecke Poverty Measures Reveal More," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1481-1503, December.
    19. Higbee, Joshua D. & Jensen, Jonathan E. & McDonald, James B., 2019. "The asymmetric log-Laplace distribution as a limiting case of the generalized beta distribution," Statistics & Probability Letters, Elsevier, vol. 151(C), pages 73-78.

    More about this item

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E25 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Aggregate Factor Income Distribution

    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:bla:revinw:v:59:y:2013:i:2:p:360-374. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/iariwea.html .

    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.