Testing procedures for detection of linear dependencies in efficiency models
AbstractThe validity of many efficiency measurement methods rely upon the assumption that variables such as input quantities and output mixes are independent of (or uncorrelated with) technical efficiency, however few studies have attempted to test these assumptions. In a recent paper, Wilson (2003) investigates a number of independence tests and finds that they have poor size properties and low power in moderate sample sizes. In this study we discuss the implications of these assumptions in three situations: (i) bootstrapping non-parametric efficiency models; (ii) estimating stochastic frontier models and (iii) obtaining aggregate measures of industry efficiency. We propose a semi-parametric Hausmann-type asymptotic test for linear independence (uncorrelation), and use a Monte Carlo experiment to show that it has good size and power properties in finite samples. We also describe how the test can be generalized in order to detect higher order dependencies, such as heteroscedasticity, so that the test can be used to test for (full) independence when the efficiency distribution has a finite number of moments. Finally, an empirical illustration is provided using data on US electric power generation.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 198 (2009)
Issue (Month): 2 (October)
Contact details of provider:
Web page: http://www.elsevier.com/locate/eor
Data envelopment analysis Correlation Independence Hypothesis test Aggregation;
Other versions of this item:
- Antonio Peyrache & Tim Coelli, 2008. "Testing procedures for detection of linear dependencies in efficiency models," CEPA Working Papers Series WP022008, School of Economics, University of Queensland, Australia.
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Keshvari, Abolfazl & Kuosmanen, Timo, 2013. "Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation," European Journal of Operational Research, Elsevier, vol. 231(2), pages 481-491.
- Karagiannis, Giannis, 2012. "More on the Fox paradox," Economics Letters, Elsevier, vol. 116(3), pages 333-334.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If references are entirely missing, you can add them using this form.