Free Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 2, March-April 2021
Page(s) 481 - 493
DOI https://doi.org/10.1051/ro/2021017
Published online 31 March 2021
  • D.J. Aigner and S.F. Chu, On estimating the industry production function. Am. Econ. Rev. 58 (1968) 826–839. [Google Scholar]
  • D.J. Aigner, C.A.K. Lovell and P. Schmidt, Formulation and estimation of stochastic. Rev. Econ. Stat. 80 (1977) 454–465. [Google Scholar]
  • A.G. Assaf and A. Josiassen, Frontier analysis: a state-of-the-art review and meta-analysis. J. Travel Res. 55 (2016) 612–627. [Google Scholar]
  • G.E. Battese and T.J. Coelli, Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. J. Prod. Anal. 3 (1992) 153–169. [Google Scholar]
  • G.E. Battese and T.J. Coelli, A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Econ. 20 (1995) 323–332. [Google Scholar]
  • M. Blix, S. Daltung and L. Heikensten, On central bank efficiency. Sveriges Riksbank Econ. Rev. 3 (2003) 81–93. [Google Scholar]
  • K.A. Bollen and M.D. Noble, Structural equation models and the quantification of behavior. Proc. Nat. Acad. Sci. 108 (2011) 15639–15646. [Google Scholar]
  • S.G. Cecchetti and S. Krause, Central bank structure, policy efficiency, and macroeconomic performance: exploring empirical relationships. Rev.-Federal Res. Bank Saint Louis 48 (2002) 47–60. [Google Scholar]
  • W.W. Chin, Commentary: issues and opinion on structural equation modeling. JSTOR 22 (1998) 7–16. [Google Scholar]
  • T.J. Coelli, A guide to FRONTIER version 4.1: a computer program for stochastic frontier production and cost function estimation. CEPA Working papers 7 (1996) 1–33. [Google Scholar]
  • T.J. Coelli, D.S.P. Rao, C.J. O’Donnell and G.E. Battese, An introduction to Efficiency and Productivity Analysis. Springer Science & Business Media (2005). [Google Scholar]
  • T. Coelli, L. Lauwers and G. Van Huylenbroeck, Formulation of technical, economic and environmental efficiency measures that are consistent with the materials balance condition. Centre for Efficiency and Productivity Analysis Working Paper 6 (2005). [Google Scholar]
  • Q.F. Dar and A.Y. Hyo, Investigate Central Bank Efficiency and its relation with export level: a case of Top Asian Exporter Countries. Int. Conf. Korean Trade Assoc. 8 (2019) 695–705. [Google Scholar]
  • Q.F. Dar, T.R. Padi and A.M. Tali, Decision support system through data envelopment analysis & stochastic frontier analysis. Int. J. Modern Math. Sci. 15 (2017) 1–13. [Google Scholar]
  • Q.F. Dar, Y.H. Ahn and G.F. Dar, Impact of international trade on central bank efficiency: an application of DEA and Tobit Regression Analysis. Stat. Optim. Inf. Comput. 9 (2021) 223–240. [Google Scholar]
  • M.J. Farrell, The measurement of productive efficiency. J. R. Stat. Soc.: Ser. A 120 (1957) 253–281. [Google Scholar]
  • T. Feng, J. Zhang and A. Fujiwara, Environmental efficiency analysis of transportation system: a stochastic frontier approach with flexible cause-effect structure. Proc. Eastern Asia Soc. Transp. Stud. 7 (2007) 188–288. [Google Scholar]
  • E. Fusco and F. Vidoli, Spatial stochastic frontier models: controlling spatial global and local heterogeneity. Int. Rev. Appl. Econ. 27 (2013) 679–694. [Google Scholar]
  • H. Gunes and D. Yildirim, Estimating cost efficiency of Turkish commercial banks under unobserved heterogeneity with stochastic frontier models. Central Bank Rev. 16 (2016) 127–136. [Google Scholar]
  • L. Heikensten, How to promote and measure central bank efficiency. BIS Rev. 24 (2003) 1–5. [Google Scholar]
  • R.H. Hoyle, The Structural Equation Modeling Approach: Basic Concepts and Fundamental Issues. Sage Publications, Inc. (1995). [Google Scholar]
  • M.E.F. Ihaddaden, Investigating eurosystem central banking efficiency: a data envelopment analysis approach. Rev. Econ. Gestion 3 (2019) 1–12. [Google Scholar]
  • P.M. Jackson, M.D. Fethi and G. Inal, Evaluating the Efficiency of Turkish Commercial Banks: An Application of DEA and Tobit Analysis. University of Leicester Efficiency and Productivity Research Unit (2000). [Google Scholar]
  • R.I. Jennrich and P.F. Sampson, Rotation for simple loadings. Psychometrika 31 (1966) 313–323. [PubMed] [Google Scholar]
  • S.C. Kumbhakar and C.K. Lovell, Stochastic Frontier Analysis. Cambridge University Press (2003). [Google Scholar]
  • H. Leibenstein, Beyond Economic Man. Harvard University Press (1976). [Google Scholar]
  • W. Meeusen and J. van Den Broeck, Efficiency estimation from Cobb-Douglas production functions with composed error. Int. Econ. Rev. 18 (1977) 435–444. [Google Scholar]
  • V. McKinley and K. Banaian, Central Bank Operational Efficiency: Meaning and Measurement. Central Banking Publications (2005) 44–81. [Google Scholar]
  • G. Ravishankar and M.M. Stack, The gravity model and trade efficiency: a stochastic frontier analysis of Eastern European Countries’ potential trade. World Econ. 37 (2014) 690–704. [Google Scholar]
  • S.P. Rossi, M. Schwaiger and G. Winkler, Managerial behavior and cost/profit efficiency in the banking sectors of Central and Eastern European countries. Working Papers 96 (2005). [Google Scholar]
  • L. Simar, I. Van Keilegom and V. Zelenyuk, Non-parametric least-squares methods for stochastic frontier models. J. Prod. Anal. 47 (2017) 189–204. [Google Scholar]
  • C.P. Timmer, Using a probabilistic frontier production function to measure technical efficiency. J. Political Econ. 79 (1971) 776–794. [Google Scholar]
  • S. Wright, The method of path coefficients. Ann. Math. Stat. 5 (1934) 161–215. [Google Scholar]
  • W. Xiping and L. Yuesheng, Banking efficiency in China: application of DEA and Tobit Analysis. In: International Conference of Management Science and Engineering of IEEE (2007). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.