When, where and how to perform efficiency estimation

Journal of the Royal Statistical Society. Series A: Statistics in Society

Volumn 175, Issue 4, 2012, Pages 863-892

  Badunenko, Oleg ^a   Henderson, Daniel J ^b   Kumbhakar, Subal C ^b  

^a UNIVERSITY OF COLOGNE   (Germany)

^b BINGHAMTON UNIVERSITY   (United States)

Author keywords

Bootstrap; Non parametric kernel; Technical efficiency

Indexed keywords

EID: 84867140082     PISSN: 09641998     EISSN: 1467985X     Source Type: Journal    
DOI: 10.1111/j.1467-985X.2011.01023.x     Document Type: Article

References (39)

1. Aigner, D., Lovell, C. K. and Schmidt, P. (1977) Formulation and estimation of stochastic frontier production function models. J. Econmetr., 6, 21-37.
2. Amato, L. H. and Amato, C. H. (2000) The impact of high tech production techniques on productivity and profitability in selected U.S. manufacturing industries. Rev. Indust. Organizn, 16, 327-342.
3. Banker, R. D., Gadh, V. M. and Gorr, W. L. (1993) A monte carlo comparision of two production frontier estimation methods: corrected ordinary least squares and data development analysis. Eur. J. Oper. Res., 167, 332-343.
4. Berndt, E. R. and Morrison, C. J. (1995) High-tech capital formation and economic performance in U.S. manufacturing industries: an exploratory analysis. J. Econmetr., 65, 9-43.
5. Bojanic, A. N., Caudill, S. B. and Ford, J. M. (1998) Small-sample properties of ML, COLS, and DEA estimators of frontier models in the presence of heteroscedasticity. Eur. J. Oper. Res., 180, 140-148.
6. Charnes, A., Cooper, W. W. and Rhodes, E. (1978) Measuring the efficiency of decision-making units. Eur. J. Oper. Res., 84, 655-676.
7. Cubbin, J. (2005) Efficiency in the water industries. Util. Poly, 13, 289-293.
8. Cubbin, J. and Tzanidakis, G. (1998) Regression versus Data Envelopment Analysis for efficiency measurement: an application to the England and Wales regulated water industry. Util. Poly, 7, 63-119.
9. Dassler, T., Parker, D. and Saal, D. S. (2006) Methods and trends of performance benchmarking in UK utility regulation. Util. Poly, 14, 166-174.
10. Fan, Y., Li, Q. and Weersink, A. (1996) Semiparametric estimation of stochastic production frontier models. J. Bus. Econ. Statist., 14, 460-468.
11. Farrell, M. J. (1957) The measurement of productive efficiency (with discussion). J. R. Statist. Soc. A, 120, 253-290.
12. Gong, B.-H. and Sickles, R. (1992) Finite sample evidence on the performance of stochastic frontiers and Data Envelopment Analysis using panel data. J. Econmetr., 51, 259-284.
13. Guilkey, D. K., Lovell, C. A. K. and Sickles, R. C. (1983) A comparison of the performance of three flexible functional forms. Int. Econ. Rev., 24, 591-616.
14. Hastie, T. and Tibshirani, R. J. (1993) Varying-coefficient models (with discussion). J. R. Statist. Soc. B, 55, 757-796.
15. Hayfield, T. and Racine, J. S. (2008) Nonparametric econometrics: the np package. J. Statist. Softwr., 27, 1-32.
16. Henderson, D. J. and Russell, R. R. (2005) Human capital and convergence: a production frontier approach. Int. Econ. Rev., 46, 1167-1205.
17. Horrace, W. C. and Schmidt, P. (1996) Confidence statements for efficiency estimates from stochastic frontier models. J. Productvty Anal., 7, 257-282.
18. Hurvich, C. M., Simonoff, J. S. and Tsai, C.-L. (1998) Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J. R. Statist. Soc. B, 60, 271-293.
19. Jondrow, J., Knox Lovell, C. A., Materov, I. S. and Schmidt, P. (1982) On the estimation of technical inefficiency in the stochastic frontier production function model. J. Econmetr., 19, 233-238.
20. Kneip, A. and Simar, L. (1996) A general framework for frontier estimation with panel data. J. Productvty Anal., 7, 187-212.
21. Kneip, A., Simar, L. and Wilson, P. W. (2008) Asymptotics for dea estimators in non-parametric frontier models. Econmetr. Theor., 24, 1663-1697.
22. Kumbhakar, S. C. and Lovell, C. A. K. (2003) Stochastic Frontier Analysis. Cambridge: Cambridge University Press.
23. Kumbhakar, S. C. and Tsionas, E. G. (2011) Stochastic error specification in primal and dual production systems. J. Appl. Econmetr., 26, 270-297.
24. Lien, G., Størdal, S. and Baardsen, S. (2007) Technical efficiency in timber production and effects of other income sources. Small-scale Forest., 6, 65-78.
25. Meeusen, W. and van den Broeck, J. (1977) Efficiency estimation from Cobb-Douglas production functions with composed error. Int. Econ. Rev., 18, 435-444.
26. Morrison Paul, C. J., Johnston, W. E. and Frengley, G. A. G. (2000) Efficiency in New Zealand sheep and beef farming: the impacts of regulatory reform. Rev. Econ. Statist., 82, 325-337.
27. Park, S.-U. and Lesourd, J.-B. (2000) The efficiency of conventional fuel power plants in South Korea: a comparison of parametric and non-parametric approaches. Int. J. Prod. Econ., 63, 59-67.
28. R Development Core Team (2008) R: a Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.
29. Ruggiero, J. (2007) A comparison of dea and stochastic frontier model using panel data. Int. Trans. Oper. Res., 14, 259-266.
30. Schmidt, P. and Lin, T.-F. (1984) Simple tests of alternative specifications in stochastic frontier models. J. Econmetr., 24, 349-361.
31. Shephard, R. W. (1970) Theory of Cost and Production Functions. Princeton: Princeton University Press.
32. Simar, L. and Wilson, P. (1998) Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Mangmnt Sci., 44, 49-61.
33. Simar, L. and Wilson, P. (2000) A general methodology for bootstrapping in non-parametric frontier models. J. Appl. Statist., 27, 779-802.
34. Smith, P. C. and Street, A. (2005) Measuring the efficiency of public services: the limits of analysis. J. R. Statist. Soc. A, 168, 401-417.
35. Stone, M. (2002) How not to measure the efficiency of public services (and how one might) (with discussion). J. R. Statist. Soc. A, 165, 405-434.
36. Thanassoulis, E. (1993) A comparison of regression analysis and data envelopment analysis as alternative methods for performance assessments. J. Oper. Res. Soc., 44, 1129-1144.
37. Thanassoulis, E. (2000a) DEA and its use in the regulation of water companies. Eur. J. Oper. Res., 127, 1-13.
38. Thanassoulis, E. (2000b) The use of Data Envelopment Analysis in the regulation of UK water utilities: water distribution. Eur. J. Oper. Res., 126, 436-453.
39. Wilson, P. W. (2008) FEAR: a software package for frontier efficiency analysis with R. Socio-econ. Planng Sci., 42, 247-254.