EconPapers    
Economics at your fingertips  
 

Influence function-based empirical likelihood and generalized confidence intervals for the Lorenz curve

Yuyin Shi, Bing Liu and Gengsheng Qin ()
Additional contact information
Yuyin Shi: Georgia State University
Bing Liu: Georgia State University
Gengsheng Qin: Georgia State University

Statistical Methods & Applications, 2020, vol. 29, issue 3, No 1, 427-446

Abstract: Abstract This paper aims to solve confidence interval estimation problems for the Lorenz curve. First, we propose new nonparametric confidence intervals using the influence function-based empirical likelihood method. We show that the limiting distributions of the empirical log-likelihood ratio statistics for the Lorenz ordinates are standard chi-square distributions. We also develop “exact” parametric intervals for the Lorenz ordinate based on generalized pivotal quantities when the underlying income distribution is a Pareto distribution or a Lognormal distribution. Extensive simulation studies are conducted to evaluate the finite sample performances of the proposed methods. Finally, we apply our methods to a real income dataset.

Keywords: Empirical likelihood; Influence function; Generalized pivotal quantities; The Lorenz curve (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s10260-019-00482-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:29:y:2020:i:3:d:10.1007_s10260-019-00482-w

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-019-00482-w

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2020-09-26
Handle: RePEc:spr:stmapp:v:29:y:2020:i:3:d:10.1007_s10260-019-00482-w