Two flexible functional form approaches for approximating the Lorenz curve.

This paper introduces two flexible form approaches to approximate Lorenz curves. The first approach expands the inverse function of an income distribution in an exponential polynomial series and derives the Lorenz curve from it. The required convexity condition can be imposed using a Bayesian meth...

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Bibliographic Details
Published in:Journal of Econometrics Vol. 72; pp. 251 - 275
Main Authors: Ryu, Hang K., Slottje, Daniel Jonathan
Format: Article
Published: Elsevier Science May/June 1996
Subjects:
Online Access:View this record in EBSCOhost
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      dt: May/June 1996
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      pub: Elsevier Science
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        10.1016/0304-4076(94)01722-0
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        atl: Two flexible functional form approaches for approximating the Lorenz curve.
      aug:
        au:
          Ryu, Hang K.
          Slottje, Daniel Jonathan
      su:
        Lorenz curve
        Bayesian analysis
        Statistical hypothesis testing
        Polynomials
        Income inequality
        United States
        South Korea
      sug:
        subj:
          United States
          South Korea
          Lorenz curve
          Bayesian analysis
          Statistical hypothesis testing
          Polynomials
          Income inequality
      ab: This paper introduces two flexible form approaches to approximate Lorenz curves. The first approach expands the inverse function of an income distribution in an exponential polynomial series and derives the Lorenz curve from it. The required convexity condition can be imposed using a Bayesian method. The second approach approximates the Lorenz curve with a sequence of Berstein polynomial functions. The required convexity condition is automatically established in this approach. We compare these approaches with other well-known fixed functional form approaches. We evaluate the performance of these functional forms by comparing approximation errors, maximum error, and the estimates of the Gini coefficient produced by various approaches. Reprinted by permission of the publisher.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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