EconPapers    
Economics at your fingertips  
 

Technical Efficiency of Australian Wool Production: Point and Confidence Interval Estimates

Iain Fraser () and William Horrace ()

Journal of Productivity Analysis, 2003, vol. 20, issue 2, 169-190

Abstract: A balanced panel of data is used to estimate technical efficiency, employing a fixed-effects stochastic frontier specification for wool producers in Australia. Both point estimates and confidence intervals for technical efficiency are reported. The confidence intervals are constructed using the multiple comparisons with the best (MCB) procedure of Horrace and Schmidt (1996, 2000). The confidence intervals make explicit the precision of the technical efficiency estimates and underscore the dangers of drawing inferences based solely on point estimates. Additionally, they allow identification of wool producers that are statistically efficient and those that are statistically inefficient. The data reveal at the 95% level that twenty-one of the twenty-six wool farms analyzed may be efficient. Copyright Kluwer Academic Publishers 2003

Keywords: production functions; stochastic frontier; multiple comparisons; wool (search for similar items in EconPapers)
Date: 2003
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1023/A:1025180205923 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Technical Efficiency of Australian Wool Production: Point and Confidence Interval Estimates (2003) Downloads
Working Paper: Technical Efficiency of Australian Wool Production: Point and Confidence Interval Estimates (2002) Downloads
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:kap:jproda:v:20:y:2003:i:2:p:169-190

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/11123/PS2

Access Statistics for this article

Journal of Productivity Analysis is currently edited by William Greene, Chris O'Donnell and Victor Podinovski

More articles in Journal of Productivity Analysis from Springer
Bibliographic data for series maintained by Sonal Shukla ().

 
Page updated 2019-11-06
Handle: RePEc:kap:jproda:v:20:y:2003:i:2:p:169-190