One-stage and two-stage DEA estimation of the effects of contextual variables

European Journal of Operational Research

Volumn 220, Issue 2, 2012, Pages 559-570

  Johnson, Andrew L ^a   Kuosmanen, Timo ^b  

^a Texas AandM University   (United States)

^b HELSINKI SCHOOL OF ECONOMICS   (Finland)

Author keywords

Data envelopment analysis; Partial linear model; Semi nonparametric regression

Indexed keywords

DATA ENVELOPMENT ANALYSIS; EFFICIENCY; INTELLIGENT SYSTEMS; REGRESSION ANALYSIS; SAMPLING;

CONTEXTUAL VARIABLES; CONVEX NONPARAMETRIC LEAST SQUARES; FINITE SAMPLES; OPERATIONAL CONDITIONS; PARTIAL LINEAR MODELS; PRODUCTIVE EFFICIENCY ANALYSIS; SEMI-NONPARAMETRIC REGRESSIONS; STATISTICAL PROPERTIES;

MONTE CARLO METHODS;

EID: 84859806111     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2012.01.023     Document Type: Article

References (31)

1. Afriat, S. N., 1967. The construction of a utility function from expenditure data. International Economic Review 8, 67-77.
2. Afriat, S. N., 1972. Efficiency estimation of production functions. International Economic Review 13(3), 568-598.
3. Banker, R. D., 1993. Maximum likelihood, consistency and data envelopment analysis: a statistical foundation. Management Science 39, 1265-1273.
4. Banker, R. D., Morey, R. C., 1986. Efficiency analysis for exogenously fixed inputs and outputs. Operations Research 34(4), 513-521.
5. Banker, R. D., Natarajan, R., 2008. Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research 56(1), 48-58. (Pubitemid 351463108)
6. Chen, X., 2007. Large sample sieve estimation of semi-nonparametric models. In: Heckman, J., Leamer, E. (Eds.), Handbook of Econometrics, vol. 6. North Holland.
7. Chen, W.-C., Johnson, A. L., 2010. A unified model for detecting efficient and inefficient outliers in data envelopment analysis. Computers and Operations Research 37(2), 417-425.
8. Cooper, W. W., Seiford, L. M., Tone, K., 2007. Data Envelopment Analysis a Comprehensive Text with Models, Applications, References and DEA-Solver Software. Springer, New York.
9. Greene, W., 2008. Econometric Analysis, sixth ed. Prentice Hall.
10. Gstach, D., 1998. Another approach to data envelopment analysis in noisy environments: DEA+. Journal of Productivity Analysis 9, 161-176.
11. Hanson, D. L., Pledger, G., 1976. Consistency in concave regression. Annals of Statistics 4(6), 1038-1050.
12. Hildreth, C., 1954. Point estimates of ordinates of concave functions. Journal of the American Statistical Association 49(267), 598-619.
13. Hoff, A., 2007. Second stage DEA: comparison of approaches for modelling the DEA score. European Journal of Operational Research 181(1), 425-435.
14. Johnson, A. L., Kuosmanen, T., 2011. One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-n consistent StoNEZD method. Journal of Productivity Analysis 36(2), 219-230.
15. Johnson, A. L., McGinnis, L. F., 2008. Outlier detection in two-stage semiparameteric DEA models. European Journal of Operational Research 187(2), 629-635. (Pubitemid 350102861)
16. Kuosmanen, T., 2006. Stochastic nonparametric envelopment of data: combining virtues of SFA and DEA in a unified framework. MTT Disucssion Papers. Helsinki, Finland.
17. Kuosmanen, T., 2008. Representation theorem for convex nonparametric least squares. Econometrics Journal 11, 308-325.
18. Kuosmanen, T., 2011. Stochastic Semi-nonparametric Efficiency Analysis of Electricity Distribution Networks: Application of the StoNED Method in the Finnish Regulatory Model, SSRN Working Paper. .
19. Kuosmanen, T., Johnson, A. L., 2010. Data envelopment analysis as nonparametric least squares regression. Operations Research 58(1), 149-160.
20. Kuosmanen, T., Kortelainen, M., in press. Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints. Journal of Productivity Analysis. doi:10.1007/s11123-010-0201-3.
21. Kuosmanen, T., Cherchye, L., Sipilainen, T., 2006. The law of one price in data envelopment analysis: restricting weight flexibility across firms. European Journal of Operational Research 170(3), 735-757. (Pubitemid 41607634)
22. Kuosmanen, T., Kortelainen, M., Kultti, K., Pursiainen, H., Saastamoinen, A., Sipiläinen, T., 2010. Sähköverkkotoiminnan kustannustehokkuuden estimointi StoNED-menetelmällä: Ehdotus tehostamistavoitteiden ja kohtuullisten kustannusten arviointiperusteiden kehittämiseksi kolmannella valvontajaksolla 2012-2015, Energiamarkkinaviraston julkaisu 31.8.2010 (in Finnish). .
23. Lee, C.-Y., Johnson, A. L., Moreno-Centeno, E., Kuosmanen, T., 2011. A More Efficient Algorithm for Convex Nonparametric Least Squares, SSRN Working Paper. .
24. Li, S.-K., Ng, Y. C., 1995. Measuring the productive efficiency of a group of firms. International Advances in Economic Research 1(4), 377-390.
25. McDonald, J., 2009. Using least squares and tobit in second stage DEA efficiency analyses. European Journal of Operational Research 197(2), 792-798.
26. Ray, S. C., 1988. Data envelopment analysis, nondiscretionary inputs and efficiency: an alternative interpretation. Socio-Economic Planning Sciences 22(4), 167-176.
27. Rousseeuw, P. J., Leroy, A. M., 1987. Robust Regression and Outlier Detection. John Wiley & Sons, New York.
28. Ruggiero, J., 1998. Non-discretionary inputs in data envelopment analysis. European Journal of Operational Research 111(3), 461-469. (Pubitemid 128410905)
29. Simar, L., Wilson, P. W., 2007. Estimation and inferencein two-stage, semi-parametric models of production processes. Journal of Econometrics 136(1), 31-64. (Pubitemid 44380324)
30. Simar, L., Wilson, P. W., 2011. Two-stage DEA: caveat emptor. Journal of Productivity Analysis 36(2), 205-218.
31. Wang, H. J., Schmidt, P., 2002. One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels. Journal of Productivity Analysis 18(2), 129-144. (Pubitemid 37131015)