References
- Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37. https://doi.org/10.1016/0304-4076(77)90052-5
- Ayadi, R., Naceur, S. B., Casu, B., & Quinn, B. (2016). Does Basel compliance matter for bank performance? Journal of Financial Stability, 23, 15–32. https://doi.org/10.1016/j.jfs.2015.12.007
- Banker, R. D. (1993). Maximum likelihood, consistency and data envelopment analysis: A statistical foundation. Management Science, 39(10), 1265–1273. https://doi.org/10.1287/mnsc.39.10.1265
- Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
- Banker, R. D., & Maindiratta, A. (1992). Maximum likelihood estimation of monotone and concave production frontiers. Journal of Productivity Analysis, 3(4), 401–415. https://doi.org/10.1007/BF00163435
- Banker, R. D., & Natarajan, R. (2008). Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research, 56(1), 48–58. https://doi.org/10.1287/opre.1070.0460
- Banker, R. D., Zheng, Z., & Natarajan, R. (2010). DEA-based hypothesis tests for comparing two groups of decision making units. European Journal of Operational Research, 206(1), 231–238. https://doi.org/10.1016/j.ejor.2010.01.027
- Barth, J. R., Lin, C., Ma, Y., Seade, J., & Song, F. M. (2013). Do bank deregulation, supervision and monitoring enhance or impede bank efficiency? Journal of Banking & Finance, 37(8), 2879–2892. https://doi.org/10.1016/j.jbankfin.2013.04.030
- Bogetoft, P., & Otto, L. (2011). Benchmarking with DEA, SFA, and R. Springer.
- Casu, B., & Girardone, C. (2004). Financial conglomeration: Efficiency, productivity and strategic drive. Applied Financial Economics, 14(10), 687–696. https://doi.org/10.1080/0960310042000243529
- Casu, B., & Molyneux, P. (2003). A comparative study of efficiency in European banking. Applied Economics, 35(17), 1865–1876. https://doi.org/10.1080/0003684032000158109
- Charnes, A., & Cooper, W. W. (1963). Deterministic equivalents for optimizing and satisficing under chance constraints. Operations Research, 11(1), 18–39. https://doi.org/10.1287/opre.11.1.18
- Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
- Chilingerian, J. A. (1995). Evaluating physical efficiency in hospitals: A multivariate analysis of best practices. European Journal of Operational Research, 80(3), 548–574. https://doi.org/10.1016/0377-2217(94)00137-2
- Chortareas, G. E., Girardone, C., & Ventouri, A. (2012). Bank supervision, regulation, and efficiency: Evidence from the European Union. Journal of Financial Stability, 8(4), 292–302. https://doi.org/10.1016/j.jfs.2011.12.001
- Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). Springer.
- Dyson, R. G., & Shale, E. A. (2010). Data envelopment analysis, operational research and uncertainty. Journal of the Operational Research Society, 61(1), 25–34. https://doi.org/10.1057/jors.2009.145
- Efron, B., & Tibshirani, R. J. (1998). An introduction to the bootstrap. Chapman & Hall/CRC.
- Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4–8. https://doi.org/10.1016/j.seps.2017.01.008
- Eskelinen, J., & Kuosmanen, T. (2013). Intertemporal efficiency analysis of sales teams of a bank: Stochastic semi-nonparametric approach. Journal of Banking & Finance, 37(12), 5163–5175. https://doi.org/10.1016/j.jbankfin.2013.03.010
- Friesner, D., Mittelhammer, R., & Rosenman, R. (2013). Inferring the incidence of industry inefficiency from DEA estimates. European Journal of Operational Research, 224(2), 414–424. https://doi.org/10.1016/j.ejor.2012.08.003
- Fujii, H., Managi, S., & Matousek, R. (2014). Indian bank efficiency and productivity changes with undesirable outputs: A disaggregated approach. Journal of Banking & Finance, 38, 41–50. https://doi.org/10.1016/j.jbankfin.2013.09.022
- Gajewski, B. J., Lee, R., Bott, M., Piamjariyakul, U., & Taunton, R. L. (2009). On estimating the distribution of data envelopment analysis efficiency scores: An application of nursing homes’ care planning process. Journal of Applied Statistics, 36(9), 933–944. https://doi.org/10.1080/02664760802552986
- Havrylchyk, O. (2006). Efficiency of the Polish banking industry: Foreign versus domestic banks. Journal of Banking & Finance, 30(7), 1975–1996. https://doi.org/10.1016/j.jbankfin.2005.07.009
- Kneip, A., Park, B. U., & Simar, L. (1998). A note on the convergence of nonparametric DEA estimators for production efficiency scores. Econometric Theory, 14(6), 783–793. https://doi.org/10.1017/S0266466698146042
- Kneip, A., Simar, L., & Wilson, P. W. (2003). Asymptotics for DEA estimators in non-parametric frontier models (Discussion Paper 0317). Institut de Statistique.
- Kneip, A., Simar, L., & Wilson, P. W. (2008). Asymptotics and consistent bootstraps for DEA estimators in nonparametric frontier models. Econometric Theory, 24(6), 1663–1697. https://doi.org/10.1017/S0266466608080651
- Kneip, A., Simar, L., & Wilson, P. W. (2011). A computationally efficient, consistent bootstrap for inference with non-parametric DEA estimators. Computational Economics, 38(4), 483–515. https://doi.org/10.1007/s10614-010-9217-z
- Kuosmanen, T. (2008). Representation theorem for convex nonparametric least squares. Econometrics Journal, 11(2), 308–325. https://doi.org/10.1111/j.1368-423X.2008.00239.x
- Kuosmanen, T., & Johnson, A. (2017). Modeling joint production of multiple outputs in StoNED: Directional distance function approach. European Journal of Operational Research, 262(2), 792–801. https://doi.org/10.1016/j.ejor.2017.04.014
- Kuosmanen, T., Johnson, A., & Saastamoinen, A. (2015). Stochastic nonparametric approach to efficiency analysis: A unified framework. In J. Zhu (Ed.), Data envelopment analysis, International series in operations research & management science (Vol. 221, pp. 191–244). Springer. https://doi.org/10.1007/978-1-4899-7553-9
- Kuosmanen, T., & Kortelainen, M. (2007). Stochastic nonparametric envelopment of data: Cross-sectional frontier estimation subject to shape constraints (Economics Discussion Paper 46). University of Joensuu. https://doi.org/10.2139/ssrn.983882
- Kuosmanen, T., & Kortelainen, M. (2012). Stochastic non-smooth envelopment of data: Semi-parametric frontier estimation subject to shape constraints. Journal of Productivity Analysis, 38(1), 11–28. https://doi.org/10.1007/s11123-010-0201-3
- Land, K. C., Knox Lovell, C. A., & Thore, S. (1993). Chance-constrained data envelopment analysis. Managerial and Decision Economics, 14(6), 541–554. https://doi.org/10.1002/mde.4090140607
- Liu, J., Sickles, R. C., & Tsionas, E. G. (2017). Bayesian treatments for panel data stochastic frontier models with time varying heterogeneity. Econometrics, 5(3), 33–21. https://doi.org/10.3390/econometrics5030033
- Liu, J. S., Lu, L. Y. Y., Lu, W. M., & Lin, B. J. Y. (2013). A survey of DEA applications. Omega, 41(5), 893–902. https://doi.org/10.1016/j.omega.2012.11.004
- Mamatzakis, E., Matousek, R., & Vu, A. N. (2016). What is the impact of bankrupt and restructured loans on Japanese bank efficiency? Journal of Banking & Finance, 72, S187–S202. https://doi.org/10.1016/j.jbankfin.2015.04.010
- Meeusen, W., & Van Den Broeck, J. (1977). Efficiency estimation from Cobb–Douglas production functions with composed error. International Economic Review, 18(2), 435–445. https://doi.org/10.2307/2525757
- Mitropoulos, P., Talias, M. A., & Mitropoulos, I. (2015). Combining stochastic DEA with Bayesian analysis to obtain statistical properties of the efficiency scores: An application to Greek public hospitals. European Journal of Operational Research, 243(1), 302–311. https://doi.org/10.1016/j.ejor.2014.11.012
- Olesen, O. B. (2006). Comparing and combining two approaches for chance constrained DEA. Journal of Productivity Analysis, 26(2), 103–119. https://doi.org/10.1007/s11123-006-0008-4
- Olesen, O. B., & Petersen, N. C. (1995). Chance constrained efficiency evaluation. Management Science, 41(3), 442–457. https://doi.org/10.1287/mnsc.41.3.442
- Pearson, E. S. (1925). Bayes’ theorem, examined in the light of experimental sampling. Biometrika, 17(3–4), 388–442. https://doi.org/10.2307/2332088
- Poirier, D. J. (1995). Intermediate statistics and econometrics: A comparative approach. The MIT Press.
- Rosenman, R., & Friesner, D. (2004). Scope and scale inefficiencies in physician practices. Health Economics, 13(11), 1091–1116. https://doi.org/10.1002/hec.882
- Sarath, B., & Maindiratta, A. (1997). On the consistency of maximum likelihood estimation of monotone and concave production frontiers. Journal of Productivity Analysis, 8(3), 239–246. https://doi.org/10.1023/A:1007725103835
- Sealey, C. W., & Lindley, J. T. (1977). Inputs, outputs, and a theory of production and cost at depository financial institutions. The Journal of Finance, 32(4), 1251–1266. https://doi.org/10.1111/j.1540-6261.1977.tb03324.x
- Sengupta, J. K. (1998). The efficiency distribution in a production cost model. Applied Economics, 30(1), 125–132. https://doi.org/10.1080/000368498326218
- Simar, L. (2007). How to improve the performances of DEA/FDH estimators in the presence of noise? Journal of Productivity Analysis, 28(3), 183–201. https://doi.org/10.1007/s11123-007-0057-3
- Simar, L., Vanhems, A., & Wilson, P. W. (2012). Statistical inference for DEA estimators of directional distances. European Journal of Operational Research, 220(3), 853–864. https://doi.org/10.1016/j.ejor.2012.02.030
- Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61. https://doi.org/10.1287/mnsc.44.1.49
- Simar, L., & Wilson, P. W. (1999). Estimating and bootstrapping Malmquist indices. European Journal of Operational Research, 115(3), 459–471. https://doi.org/10.1016/S0377-2217(97)00450-5
- Simar, L., & Wilson, P. W. (2000a). A general methodology for bootstrapping in non-parametric frontier models. Journal of Applied Statistics, 27(6), 779–802. https://doi.org/10.1080/02664760050081951
- Simar, L., & Wilson, P. W. (2000b). Statistical inference in nonparametric frontier models: The state of the art. Journal of Productivity Analysis, 13(1), 49–78. https://doi.org/10.1023/A:1007864806704
- Simar, L., & Wilson, P. W. (2009). Inferences from cross-sectional, stochastic frontier models. Econometric Reviews, 29(1), 62–98. https://doi.org/10.1080/07474930903324523
- Sklavos, S., Porrill, J., Kaneko, C. R. S., & Dean, P. (2005). Evidence for wide range of time scales in oculomotor plant dynamics: Implications for models of eye-movement control. Vision Research, 45(12), 1525–1542. https://doi.org/10.1016/j.visres.2005.01.005
- Smith, R. (1984). Efficient Monte Carlo procedures for generating points uniformly distributed over bounded regions. Operations Research, 32(6), 1296–1308. https://doi.org/10.1287/opre.32.6.1296
- Sohn, S. Y., & Choi, H. (2006). Random effects logistic regression model for data envelopment analysis with correlated decision making units. Journal of the Operational Research Society, 57(5), 552–560. https://doi.org/10.1057/palgrave.jors.2602026
- Stanton, K. R. (2002). Trends in relationship lending and factors affecting relationship lending efficiency. Journal of Banking & Finance, 26(1), 127–152. https://doi.org/10.1016/S0378-4266(00)00171-0
- Sturm, J. E., & Williams, B. (2004). Foreign bank entry, deregulation and bank efficiency: Lessons from the Australian experience. Journal of Banking & Finance, 28(7), 1775–1799. https://doi.org/10.1016/j.jbankfin.2003.06.005
- Tsionas, E. G. (2003). Combining DEA and stochastic frontier models: An empirical Bayes approach. European Journal of Operational Research, 147(3), 499–510. https://doi.org/10.1016/S0377-2217(02)00248-5
- Tsionas, E. G., & Papadakis, E. N. (2010). A Bayesian approach to statistical inference in stochastic DEA. Omega, 38(5), 309–314. https://doi.org/10.1016/j.omega.2009.02.003
- Tsionas, M. G. (2020). A coherent approach to Bayesian data envelopment analysis. European Journal of Operational Research, 281(2), 439–448. https://doi.org/10.1016/j.ejor.2019.08.039
- Tsionas, M. G., & Mallick, S. K. (2019). A Bayesian semiparametric approach to stochastic frontiers and productivity. European Journal of Operational Research, 274(1), 391–402. https://doi.org/10.1016/j.ejor.2018.10.026
- Tsionas, M. G., & Polemis, M. L. (2019). On the estimation of total factor productivity: A novel Bayesian non-parametric approach. European Journal of Operational Research, 277(3), 886–902. https://doi.org/10.1016/j.ejor.2019.03.035
- Zervopoulos, P. D., Sklavos, S., Kanas, A., & Cheng, G. (2019). A multi-parametric method for bias correction of DEA efficiency estimators. Journal of the Operational Research Society, 70(4), 655–674. https://doi.org/10.1080/01605682.2018.1457478