Testing procedures for detection of linear dependencies in efficiency models

Peyrache, A. and Coelli, T. (2009) Testing procedures for detection of linear dependencies in efficiency models. European Journal of Operational Research, 198 2: 647-654. doi:10.1016/j.ejor.2008.08.014

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Author Peyrache, A.
Coelli, T.
Title Testing procedures for detection of linear dependencies in efficiency models
Journal name European Journal of Operational Research   Check publisher's open access policy
ISSN 0377-2217
1872-6860
Publication date 2009-01-01
Year available 2008
Sub-type Article (original research)
DOI 10.1016/j.ejor.2008.08.014
Open Access Status
Volume 198
Issue 2
Start page 647
End page 654
Total pages 8
Editor J. Billaut
J. Artalejo
R. Slowinski
Place of publication Amsterdam, The Netherlands
Publisher Elsevier BV
Language eng
Subject C1
910299 Microeconomics not elsewhere classified
910499 Management and Productivity not elsewhere classified
1403 Econometrics
140104 Microeconomic Theory
Abstract The 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. 2008 Elsevier B.V. All rights reserved.
Keyword Data envelopment analysis
Correlation
Independence
Hypothesis test
Aggregation
Q-Index Code C1
Q-Index Status Confirmed Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2009 Higher Education Research Data Collection
Excellence in Research Australia (ERA) - Collection
School of Economics Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
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Created: Fri, 17 Apr 2009, 00:13:46 EST by Kaelene Matts on behalf of School of Economics