Nonparametric conditional efficiency measures: Asymptotic properties

Annals of Operations Research

Volumn 173, Issue 1, 2010, Pages 105-122

  Jeong, Seok Oh ^a   Park, Byeong U ^b   Simar, Léopold ^c  

^a Hankuk University of Foreign Studies   (South Korea)

^b Seoul National University   (South Korea)

^c UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

Author keywords

Asymptotic distribution; Conditional DEA; Conditional FDH; Conditional frontier; Environmental variable; Frontier model

Indexed keywords

EID: 76649104779     PISSN: 02545330     EISSN: 15729338     Source Type: Journal    
DOI: 10.1007/s10479-008-0359-5     Document Type: Article

References (36)

1. Adolphson, D. L., Cornia, G. C., & Walters, L. C. (1991). A unified framework for classifying DEA models. Operations Research, 90, 647-657.
2. Banker, R. D., & Morey, R. C. (1986). Efficiency analysis for exogenously fixed inputs and outputs. Operations Research, 34, 513-521.
3. Bhattacharyya, A., Lovell, C. A. K., & Sahay, P. (1997). The impact of liberalization on the productive efficiency of Indian commercial banks. European Journal of Operational Research, 98, 332-347. doi: 10. 1016/S0377-2217(96)00351-7.
4. Bonaccorsi, A., Daraio, C., & Simar, L. (2006). Size, scope and trade-off in the productivity of universities: an application of robust nonparametric methods to Italian data. Scientometrics, 66(2), 389-410. doi: 10. 1007/s11192-006-0028-x.
5. Bonaccorsi, A., Daraio, C., & Simar, L. (2007a). Efficiency and productivity in European Universities. Exploring trade-offs in the strategic profile. In A. Bonaccorsi & C. Daraio (Eds.), Universities as strategic units. Productivity and efficiency patterns in the European university system. Edward Elgar PRIME collection.
6. Bonaccorsi, A., Daraio, C., Räty, T., & Simar, L. (2007b). Efficiency and University size: discipline-wise evidence from the European Universities. In A. Kangasharju (Ed.), Hyvinvointipalvelujen tuottavuus: tuloksia opintien varrelta. Helsinki: VATT.
7. Cazals, C., Florens, J. P., & Simar, L. (2002). Nonparametric frontier estimation: a robust approach. Journal of Econometrics, 106, 1-25. doi: 10. 1016/S0304-4076(01)00080-X.
8. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the inefficiency of decision making units. European Journal of Operational Research, 2, 429-444. doi: 10. 1016/0377-2217(78)90138-8.
9. Daraio, C., & Simar, L. (2005). Introducing environmental variables in nonparametric frontier models: a probabilistic approach. Journal of Productivity Analysis, 24, 93-121. doi: 10. 1007/s11123-005-3042-8.
10. Daraio, C., & Simar, L. (2006). A robust nonparametric approach to evaluate and explain the performance of mutual funds. European Journal of Operational Research, 175(1), 516-542. doi: 10. 1016/j. ejor. 2005. 06. 010.
11. Daraio, C., & Simar, L. (2007a). Advanced robust and nonparametric methods in efficiency analysis. New-York: Springer.
12. Daraio, C., & Simar, L. (2007b). Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach. Journal of Productivity Analysis, 28, 13-32. doi: 10. 1007/s11123-007-0049-3.
13. Deprins, D., Simar, L., & Tulkens, H. (1984). Measuring labor inefficiency in post offices. In M. Marchand, P. Pestieau, & H. Tulkens (Eds.), The performance of public enterprises: concepts and measurements. Amsterdam: North-Holland.
14. Fan, J., Yao, Q., & Tong, H. (1996). Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems. Biometrika, 83, 189-206. doi: 10. 1093/biomet/83. 1. 189.
15. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120, 253-281. doi: 10. 2307/2343100.
16. Fried, H. O., Lovell, C. A. K., & Vanden Eeckaut, P. (1993). Evaluating the performance of U. S. credit unions. Journal of Banking & Finance, 17, 251-265. doi: 10. 1016/0378-4266(93)90031-8.
17. Fried, H. O., Schmidt, S. S., & Yaisawarng, S. (1999). Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of Productivity Analysis, 12, 249-267. doi: 10. 1023/A: 1007800306752.
18. Gijbels, I., Mammen, E., Park, B. U., & Simar, L. (1999). On estimation of monotone and concave frontier functions. Journal of the American Statistical Association, 94, 220-228. doi: 10. 2307/2669696.
19. Hall, P., & Park, B. U. (2002). New methods for bias correction at endpoints and boundaries. The Annals of Statistics, 30, 1460-1479. doi: 10. 1214/aos/1035844983.
20. Hall, P., Racine, J., & Li, Q. (2004). Cross-validation and the estimation of conditional probability densities. Journal of the American Statistical Association, 99, 1015-1026. doi: 10. 1198/016214504000000548.
21. Jeong, S.-O. (2004). Asymptotic distribution of DEA efficiency scores. Journal of the Korean Statistical Society, 33, 449-458.
22. Jeong, S.-O., & Park, B. U. (2006). Large sample approximation of the limit distribution of convex-hull estimators of boundaries. Scandinavian Journal of Statistics, 33, 139-151. doi: 10. 1111/j. 1467-9469. 2006. 00452. x.
23. Jeong, S.-O., & Simar, L. (2006). Linearly interpolated FDH efficiency score for nonconvex frontiers. Journal of Multivariate Analysis, 97, 2141-2161. doi: 10. 1016/j. jmva. 2005. 12. 006.
24. Jones, M. C., Marron, J. S., & Sheather, S. J. (1996). A brief survey of bandwidth selection for density estimation. Journal of the American Statistical Association, 91, 401-408. doi: 10. 2307/2291420.
25. Kneip, A., Park, B. U., & Simar, L. (1998). A note on the convergence of nonparametric DEA estimators for production efficiency scores. Econometric Theory, 14, 783-793. doi: 10. 1017/S0266466698146042.
26. Kneip, A., Simar, L., & Wilson, P. W. (2003, to appear). Asymptotics for DEA estimators in nonparametric frontier models. Econometric Theory.
27. Li, Q., & Racine, J. (2007, forthcoming). Nonparametric estimation of conditional CDF and quantile functions with mixed categorical and continuous data. Journal of Business & Economic Statistics.
28. Lovell, C. A. K. (1993). Production Frontiers and Productive Efficiency. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productivity efficiency: techniques and applications. New York: Oxford University Press.
29. McCarty, T., & Yaisawarng, S. (1993). Technical efficiency in New Jersey school districts. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productivity efficiency: techniques and applications. New York: Oxford University Press.
30. Park, B. U., & Turlach, B. (1992). Practical performance of several data driven bandwidth selectors. Computational Statistics, 7, 251-270.
31. Park, B. U., Simar, L., & Weiner, C. (2000). The FDH estimator for productivity efficiency scores: Asymptotic properties. Econometric Theory, 16, 855-877. doi: 10. 1017/S0266466600166034.
32. Park, B. U., Jeong, S.-O., & Lee, Y. K. (2006). Nonparametric estimation of production efficiency. In J. Fan & H. L. Koul (Eds.), Frontiers in statistics. London: Imperial College Press.
33. Shephard, R. W. (1970). Theory of cost and production function. Princeton: Princeton University Press.
34. Simar, L., & Wilson, P. W. (2000). Statistical inference in nonparametric frontier models: The state of the art. Journal of Productivity Analysis, 13, 49-78. doi: 10. 1023/A: 1007864806704.
35. Simar, L., & Wilson, P. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31-64. doi: 10. 1016/j. jeconom. 2005. 07. 009.
36. Tulkens, H. (1993). On FDH efficiency analysis: some methodological issues and application to retail banking, courts, and urban transit. Journal of Productivity Analysis, 4, 183-210. doi: 10. 1007/BF01073473.