Abstract
Data Envelopment Analysis (DEA) measures of efficiency are very sensitive to the choice of variables for two reasons: the number of efficient firms is directly related to the number (n) of variables and the selection of the n variables greatly affects the measure of efficiency. A methodology is proposed which identifies the optimal number of variables, and which identifies the contribution of each variable to the measure of efficiency. The computer industry is used as an example to illustrate the method.
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References
Banker, R.; Charnes, A.; Cooper, W. “Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis,”Management Science, 30 (9), 1984, pp. 1078–92.
Bardhan, I.; Cooper, W.; Kumbhakar, S. “A Simulation Study of Joint Uses of Data Envelopment Analysis and Statistical Regressions for Production Function Estimation and Efficiency Evaluation,”Journal of Productivity Analysis, 9 (3), May, 1998, pp. 249–78.
Bresnahan, T.; Greenstein, S. “Technological Competition and the Structure of the Computer Industry,”Journal of Industrial Economics, 47 (1), March, 1999, pp. 1–40.
Bridges, E.; Yim, YC.; Briesch, R. “A High-Tech Product Market Share Model with Customer Expectations,”Marketing Science, 14 (1), 1995, pp. 61–81.
Charnes, A.; Cooper, A.; Rhodes, E. “Measuring the Efficiency of Decision Making Units,”European Journal of Operations Research, 2, 1978, pp. 429–44.
Farrell, M. J. “The Measurement of Productive Efficiency,”Journal of the Royal Statistical Society, Series A 120(III), 1957, pp. 253–61.
Forbes; King; Morgan. “A Small Sample Variable Selection Procedure,” Monash University, Department of Econometrics Working Paper: 15/95, 1995.
Forsund, Finn R.; Sarafoglou, Nikias. “On The Origins of Data Envelopment Analysis,”Journal of Productivity Analysis, 17, January, 2002, pp. 23–40.
Golan, A. “A Simultaneous Estimation and Variable Selection Rule,”Journal of Econometrics, 101 (1), March, 2001, pp. 165–93.
Grosskopf, S. “Statistical Inference and Nonparametric Efficiency: A Selective Survey,”Journal of Productivity Analysis, 7 (2–3), July, 1996, pp. 161–76.
Mitchell, M. “The Scale of Production in Technological Revolutions,” Federal Reserve Bank of Minneapolis Staff Report: 269, April, 2000, p. 27.
Norsworthy, J. R.; Jang, S. L. “Empirical Measurement and Analysis of Productivity and Technological Change-Applications in Hi-Technology and Service Industries Contributions to Economic Analysis series, North Holland (1992).
Sengupta, J. K. “Dynamic and Stochastic Efficiency Analysis,” World Scientific, 2000.
Thore; Kozmetsky; Phillips. “DEA of Financial Statements Data: The U.S. Computer Industry,”Journal of Productivity Analysis, 5 (3), October, 1994, pp. 229–48.
Zheng, X.; Loh, W. “Consistent Variable Selection in Linear Models,”Journal of the American Statistical Association, 90, N429, March, 1995, pp. 151–56.
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Fanchon, P. Variable selection for dynamic measures of efficiency in the computer industry. International Advances in Economic Research 9, 175–188 (2003). https://doi.org/10.1007/BF02295441
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DOI: https://doi.org/10.1007/BF02295441