IDEAS home Printed from https://ideas.repec.org/a/jid/journl/y2002v11i3-4p2-2.html
   My bibliography  Save this article

Log-Skew-Normal and Log-Skew-t Distributions as Models for Family Income Data

Author

Listed:
  • Adelchi Azzalini
  • Thomas Del Cappello
  • Samuel Kotz

Abstract

The U.S. family income data for the years 1970, 1975, 1978, 1980, 1985 and 1990 was fitted using the log-normal, Gamma, Singh-Maddala, Dagum type I and generalized Beta of second kind distributions, among others in earlier publications. Here we supplement these fittings by adding the log-skew-normal and log-skew-t distributions. In addition, we have performed similar numerical comparisons using 1997 income data collected in a sample survey from several European countries. The overall picture emerging from these numerical comparisons indicates that, while the log-skewed normal distribution provides a somewhat variable degree of goodness-of-fit, the log-skewed-t distribution seems to fit the data satisfactorily in a quite consistent way, and on the par with most creditable distributions.

Suggested Citation

  • Adelchi Azzalini & Thomas Del Cappello & Samuel Kotz, 2002. "Log-Skew-Normal and Log-Skew-t Distributions as Models for Family Income Data," Journal of Income Distribution, Ad libros publications inc., vol. 11(3-4), pages 2-2, September.
    • Handle: RePEc:jid:journl:y:2002:v:11:i:3-4:p:2-2
    as

    Download full text from publisher

    File URL: http://jid.journals.yorku.ca/index.php/jid/article/view/1249
    Download Restriction: Some fulltext downloads are only available to subscribers. See JID website for details.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Raúl Alejandro Morán-Vásquez & Anlly Daniela Giraldo-Melo & Mauricio A. Mazo-Lopera, 2023. "Quantile Estimation Using the Log-Skew-Normal Linear Regression Model with Application to Children’s Weight Data," Mathematics, MDPI, vol. 11(17), pages 1-10, August.
    2. Li, Zhengxiao & Wang, Fei & Zhao, Zhengtang, 2024. "A new class of composite GBII regression models with varying threshold for modeling heavy-tailed data," Insurance: Mathematics and Economics, Elsevier, vol. 117(C), pages 45-66.
    3. Guillermo Martínez-Flórez & Roger Tovar-Falón & Heleno Bolfarine, 2023. "The Log-Bimodal Asymmetric Generalized Gaussian Model with Application to Positive Data," Mathematics, MDPI, vol. 11(16), pages 1-14, August.

    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:jid:journl:y:2002:v:11:i:3-4:p:2-2. 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: Timm Boenke (email available below). General contact details of provider: https://edirc.repec.org/data/gyorkca.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.