EconPapers    
Economics at your fingertips  
 

Empirical Likelihood Confidence Intervals for Linear Regression Coefficients

Song Chen

Journal of Multivariate Analysis, 1994, vol. 49, issue 1, 24-40

Abstract: Nonparametric versions of Wilks' theorem are proved for empirical likelihood estimators of slope and mean parameters for a simple linear regression model. They enable us to construct empirical likelihood confidence intervals for these parameters. The coverage errors of these confidence intervals are of order n-1 and can be reduced to order n-2 by Bartlett correction.

Date: 1994
References: Add references at CitEc
Citations: View citations in EconPapers (33) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047-259X(84)71011-6
Full text for ScienceDirect subscribers only

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:eee:jmvana:v:49:y:1994:i:1:p:24-40

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Haili He ().

 
Page updated 2020-06-25
Handle: RePEc:eee:jmvana:v:49:y:1994:i:1:p:24-40