Structural and behavioral robustness in applied best-practice regulation

Socio-Economic Planning Sciences

Volumn 48, Issue 1, 2014, Pages 89-103

  Agrell, Per J ^a   Niknazar, Pooria ^b  

^a LOUVAIN SCHOOL OF MANAGEMENT   (Belgium)

^b ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

Author keywords

DEA; Energy networks; Outlier detection; Regulation

Indexed keywords

BENCHMARKING; BEST MANAGEMENT PRACTICE; DATA ENVELOPMENT ANALYSIS; ECONOMETRICS; ELECTRICITY GENERATION; ELECTRICITY SUPPLY; ENERGY MARKET; NUMERICAL MODEL; STOCHASTICITY;

GERMANY; SWEDEN;

EID: 84893722482     PISSN: 00380121     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.seps.2013.12.004     Document Type: Article

References (67)

1. Pereira de Souza M., Diallo M., Castro Souza R., Baidya T. The cost efficiency of the brazilian electricity distribution utilities: a comparison of bayesian sfa and dea models. Math Prob Eng 2010, 2010.
2. Aigner D., Lovell C.A.K., Schmidt P. Formulation and estimation of stochastic frontier production function models. JEconom 1977, 6(1):21-37.
3. Meeusen W., van Den Broeck J. Efficiency estimation from cobb-douglas production functions with composed error. Int Econom Rev 1977, 18(2):435-444.
4. Aigner D.J., Chu S.F. On estimating the industry production function. Am Econom Rev 1968, 58(4):826-839.
5. Land K.C., Lovell C.A.K., Thore S. Chance-constrained data envelopment analysis. Manag Decis Econom 1993, 14(6):541-554.
6. Watson J., Wickramanayke J., Premachandra I. The value of morningstar ratings: evidence using stochastic data envelopment analysis. Manag Financ 2011, 37(2):94-116.
7. Kuosmanen T. Stochastic semi-nonparametric frontier estimation of electricity distribution networks: application of the stoned method in the finnish regulatory model. Energy Econom 2012, 34(6):2189-2199. 10.1016/j.eneco.2012.03.005.
8. Kuosmanen T., Kortelainen M. Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints. JProduct Anal 2012, 38:11-28. 10.1007/s11123-010-0201-3.
9. Hildreth C. Point estimates of ordinates of concave functions. JAm Stat Assoc 1954, 49(267):598-619.
10. Farrell M.J. The measurement of productive efficiency. JRoyal Stat So 1957, 120:253-281.
11. Charnes A., Cooper W., Rhodes E. Measuring the efficiency of decision making units. Eur J Oper Res 1978, 2(6):429-444.
12. Banker R.D., Charnes A., Cooper W.W. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 1984, 30(9):1078-1092.
13. Cooper W.W. Data envelopment analysis: a comprehensive text with models, applications, references, and DEA-Solver software 2000, Kluwer Academic Publishers, ISBN: 9780792386933, 0792386930.
14. Bogetoft P., Otto L. Benchmarking with DEA, SFA, and R 2011, vol. 175. Springer, Dordrecht, ISBN: 1441979603, 9781441979605.
15. Deprins D., Simar L., Tulkens H. Measuring labor efficiency in post offices. The performance of public enterprises: concepts and measurements 1984, 243-267. North Holland. M.G. Marchand, P. Pestieau, H. Tulkens (Eds.).
16. Simar L., Wilson P.W. Non-parametric tests of returns to scale. Eur J Oper Res 2002, 139(1):115-132.
17. Agrell P., Tind J. Adual approach to nonconvex frontier models. JProduct Anal 2001, 16(2):129-147.
18. Bogetoft P. Dea-based yardstick competition: the optimality of best practice regulation. Annal Oper Res 1997, 73:277-298.
19. Vickers J., Yarrow G. Economic perspectives on privatization. JEconom Perspect 1991, 5(2):82.
20. Beesley M.E. Privatization, regulation and deregulation 1997, Routledge, London, ISBN: 0415164524 0415164532. 2nd ed.
21. Beesley M., Littlechild S. Privatisation: principles, problems and priorities. Lloyds Bank Rev 1983, 149:1-20.
22. Shleifer A. Atheory of yardstick competition. RAND J Econom 1985, 16(3):319-327.
23. Agrell P.J., Bogetoft P., Tind J. Dea and dynamic yardstick competition in scandinavian electricity distribution. JProduct Anal 2005, 23(2):173-201.
24. Pollitt M. Ownership and performance in electric utilities: the international evidence on privatization and efficiency 1995, Oxford Institute for Energy Studies; Oxford University Press for the Oxford Institute of Energy Studies.
25. Jamasb T., Pollitt M. Benchmarking and regulation: international electricity experience. Util Policy 2000, 9(3):107-130.
26. Haney A., Pollitt M. Efficiency analysis of energy networks: an international survey of regulators. Energy Policy 2009, 37(12):5814-5830.
27. Agrell P.J., Bogetoft P. International benchmarking of electricity transmission system operators - e3grid project 2009, Consultation report, Open Version, Council of European Energy Regulators.
28. FrontierEconomics E3grid - european tso benchmarking study 2013, Final report for CEER., Frontier Economics, Consentec, Sumicsid.
29. Bogetoft P. Incentive efficient production frontiers: an agency perspective on dea. Manag Sci 1994, 40:959-968.
30. Bogetoft P. Incentives and productivity measurements. Int J Product Econom 1995, 39:67-81.
31. Bogetoft P. Dea and activity planning under asymmetric information. JProduct Anal 2000, 13:7-48.
32. Agrell P.J., Bogetoft P. Development of benchmarking models for german electricity and gas distribution 2007, Tech. Rep, SUMICSID.
33. Shuttleworth G. Firm-specific productive efficiency: a response. Electr J 2003, 16(3):42-50.
34. Norton R.N., Sexton T.R., Silkman R.H. Firm-specific productive efficiency offsets in the development of a price cap formula. Electr J 2002, 15(10):43-52.
35. Shuttleworth G. Benchmarking of electricity networks: practical problems with its use for regulation. Util Policy 2005, 13(4):310-317.
36. Lowry M.N., Getachew L. Statistical benchmarking in utility regulation: role, standards and methods. Energy Policy 2009, 37(4):1323-1330. 10.1016/j.enpol.2008.11.027.
37. Sexton T.R. Data envelopment analysis: critique and extensions. New Dir Program Eval 1986, 32:73-105.
38. Tangeras T.P. Collusion-proof yardstick competition. JPublic Econom 2002, 83(2):231-254.
39. Mumford P. Best practice regulation ,setting targets and detecting vulnerabilities. Policy Q 2011, 7(3):36.
40. Barnett V., Lewis T. Outliers in statistical data. Wiley series in probability and mathematical statistics: applied probability and statistics 1994, Wiley & Sons.
41. Cook R., Weisberg S. Residuals and influence in regression 1982, vol. 5. Chapman and Hall, New York.
42. Dusansky R., Wilson P.W. On the relative efficiency of alternative modes of producing a public sector output: the case of the developmentally disabled. Eur J Oper Res 1995, 80(3):608-618.
43. Cook R.D. Detection of influential observation in linear regression. Technometrics 1977, 19(1):15-18.
44. Wisnowski J., Montgomery D., Simpson J. Acomparative analysis of multiple outlier detection procedures in the linear regression model. Comput Stat Data Anal 2001, 36(3):351-382.
45. Wilson P.W. Detecting outliers in deterministic nonparametric frontier models with multiple outputs. JBus Econom Stat 1993, 11(3):319-323.
46. Pastor J., Ruiz J., Sirvent I. Astatistical test for detecting influential observations in dea. Eur J Oper Res 1999, 115(3):542-554.
47. Andersen P., Petersen N.C. Aprocedure for ranking efficient units in data envelopment analysis. Manag Sci 1993, 39(10):1261-1264.
48. Seiford L., Zhu J. Infeasibility of super-efficiency data envelopment analysis models. INFOR J 1999, 37(2):174-187. Cited By (since 1996) 94.
49. Simar L. Detecting outliers in frontier models: a simple approach. JProduct Anal 2003, 20:391-424.
50. Cazals C., Florens J.P., Simar L. Nonparametric frontier estimation: a robust approach. JEconom 2002, 106(1):1-25.
51. Chen W., Johnson A. Aunified model for detecting efficient and inefficient outliers in data envelopment analysis. Comput Oper Res 2010, 37(2):417-425.
52. STEM Elnätföretagens kostnadseffektivitet och produktivitetsutveckling 2004, Tech. Rep. ER 10:2004, Swedish Energy Authority.
53. Färe R., Grosskopf S. Network dea. Socio-economic Plan Sci 2000, 34(1):35-49.
54. Agrell P.J., Hatami-Marbini A. Frontier-based performance analysis models for supply chain management: state of the art and research directions. Comput Ind Eng 2013, 66(3):567-583. URL. http://www.sciencedirect.com/science/article/pii/S036083521300065X, 10.1016/j.cie.2013.02.014.
55. Hjalmarsson L., Veiderpass A. Efficiency and ownership in swedish electricity retail distribution. JProduct Anal 1992, 3(1):7-23.
56. Seiford L.M., Zhu J. Profitability and marketability of the top 55 u.s. commercial banks. Manag Sci 1999, 45(9):1270-1288. URL. http://www.jstor.org/stable/2634837.
57. Thanssoulis E. Setting achievement targets for school children. Edu Econom 1999, 7(2):101-119.
58. Divine J. Efficiency analysis and management of not for profit and governmentally regulated organizations 1986, [Ph.D. thesis], University of Texas at Austin.
59. Bundesnetzagentur Verordnung über die anreizregulierung der energieversorgungsnetze 2007, URL. http://www.gesetze-im-internet.de/bundesrecht/aregv/gesamt.pdf.
60. Banker R. Hypothesis tests using data envelopment analysis. JProduct Anal 1996, 7(2):139-159.
61. Banker R., Gifford J. Arelative efficiency model for the evaluation of public health nurse productivity 1988, Mimeo; Carnegie Mellon University.
62. Banker R., Chang H. The super-efficiency procedure for outlier identification, not for ranking efficient units. Eur J Oper Res 2006, 175(2):1311-1320.
63. Wilson P.W. Fear: a software package for frontier efficiency analysis with r. Socio-economic Plan Sci 2008, 42(4):247-254.
64. Burns P., Jenkins C., Riechmann C. The role of benchmarking for yardstick competition. Util Policy 2005, 13(4):302-309.
65. Kumbhakar S.C., Lovell C.A.K. Stochastic frontier analysis 2003, Cambridge University Press.
66. Paradi J., Asmild M., Simak P. Using dea and worst practice dea in credit risk evaluation. JProduct Anal 2004, 21(2):153-165.
67. Rossi M., Ruzzier C. On the regulatory application of efficiency measures. Util Policy 2000, 9(2):81-92.