International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12

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 Volume 6, Issue 12 (December 2019), Pages: 112-121

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 Original Research Paper

 Title: Robustness of efficient decision-making unit based on production model of stochastic frontier analysis with different distribution error

 Author(s): Roslah Arsad 1, 2, *, Zaidi Isa 2, Ruzanna Ab Razak 3

 Affiliation(s):

 1Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Tapah Road, Perak, Malaysia
 2School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
 3Faculty of Management, Multimedia University, Cyberjaya, Selangor, Malaysia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-1080-3600

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.12.014

 Abstract:

In the empirical stochastic frontier analysis, there has been an increasing interest in exploring the consistency of the production model for decision-making units. Among it is the issue of consistency, which has been recognized as a complex process due to many factors such as different model estimations, the behavior of inefficiency effects and types of distributional errors. This paper focuses on analyses the technical efficiency of Malaysian stock performance over the period of 2013 to 2017. By utilizes SFA production function (Cobb-Douglas and Translog), which allows two decompositions of inefficiency effect into its time-variant and time-invariant, within two distributional assumptions known as truncated-normal and half-normal, which is predicted to estimate the technical efficiency score and provides a ranking efficiency based on the model estimation performance. Finally, to investigate the consistency of the estimated SFA efficiency score by examining its relationship with four models. These main findings figure out, using time-invariant inefficiency effect, Cobb-Douglas function with truncated-normal distribution more preferable for the dataset of study. By using four models with different distributional assumptions and production models, Spearman’s rank-order was implemented and revealed that there was a high degree of correlation is found between efficiency estimates that derives from the models applied. Based on the empirical study, this research shows that the ranking efficiency for selected stock performance in Malaysia was said to be robust to different kinds of distributional errors and production models. This paper provides new evidence on consistency relative efficiency of stochastic frontier model based on the three assumptions; inefficiency effect, distribution error for technical inefficiency and production function. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Efficiency, Stochastic frontier analysis, Robustness, Distribution, Performance

 Article History: Received 19 June 2019, Received in revised form 8 October 2019, Accepted 12 October 2019

 Acknowledgement:

No Acknowledgement.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Arsad R, Isa Z, and Razak RA (2019). Robustness of efficient decision-making unit based on production model of stochastic frontier analysis with different distribution error. International Journal of Advanced and Applied Sciences, 6(12): 112-121

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 Figures

 Fig. 1 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 

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 References (39) 

  1. Aigner D, Lovell CK, and 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   [Google Scholar]
  2. Battese GE and Coelli TJ (1992). Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. Journal of Productivity Analysis, 3(1-2): 153-169. https://doi.org/10.1007/BF00158774   [Google Scholar]
  3. Battese GE and Corra GS (1977). Estimation of a production frontier model: With application to the pastoral zone of Eastern Australia. Australian Journal of Agricultural Economics, 21(3): 169-179. https://doi.org/10.1111/j.1467-8489.1977.tb00204.x   [Google Scholar]
  4. Battese GE, Coelli TJ, and Colby TC (1989). Estimation of frontier production functions and the efficiencies of Indian farms using panel data from ICRISAT's village level studies (No. 400-2016-24556). In the Thirty-Third Conference of the Austra1ian Agricultural Economics Society at Lincoln College, Christchurch, New Zealand: 1-28.   [Google Scholar]
  5. Bauer PW, Berger AN, Ferrier GD, and Humphrey DB (1998). Consistency conditions for regulatory analysis of financial institutions: A comparison of frontier efficiency methods. Journal of Economics and Business, 50(2): 85-114. https://doi.org/10.1016/S0148-6195(97)00072-6   [Google Scholar]
  6. Bauman MP (2014). Forecasting operating profitability with DuPont analysis: Further evidence. Review of Accounting and Finance, 13(2): 191-205. https://doi.org/10.1108/RAF-11-2012-0115   [Google Scholar]
  7. Bhandari LC (1988). Debt/equity ratio and expected common stock returns: Empirical evidence. The Journal of Finance, 43(2): 507-528. https://doi.org/10.1111/j.1540-6261.1988.tb03952.x   [Google Scholar]
  8. Chang KJ, Chichernea DC, and HassabElnaby HR (2014). On the DuPont analysis in the health care industry. Journal of Accounting and Public Policy, 33(1): 83-103. https://doi.org/10.1016/j.jaccpubpol.2013.10.002   [Google Scholar]
  9. Coelli TJ, Rao DSP, O'Donnell CJ, and Battese GE (2005). An introduction to efficiency and productivity analysis. Springer, Boston, USA.   [Google Scholar]
  10. Cullinane K and Song DW (2006). Estimating the relative efficiency of European container ports: A stochastic frontier analysis. Research in Transportation Economics, 16: 85-115. https://doi.org/10.1016/S0739-8859(06)16005-9   [Google Scholar]
  11. Dias A (2013). Market capitalization and value-at-risk. Journal of Banking and Finance, 37(12): 5248-5260. https://doi.org/10.1016/j.jbankfin.2013.04.015   [Google Scholar]
  12. Fairfield PM and Yohn TL (2001). Using asset turnover and profit margin to forecast changes in profitability. Review of Accounting Studies, 6(4): 371-385. https://doi.org/10.1023/A:1012430513430   [Google Scholar]
  13. Ferreira N, Souza FM, and Souza AM (2014). PSI-20 portfolio efficiency analysis with SFA. International Journal of Latest Trends in Finance and Economic Sciences, 4(3): 785-789.   [Google Scholar]
  14. Gong BH and Sickles RC (1992). Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data. Journal of Econometrics, 51(1-2): 259-284. https://doi.org/10.1016/0304-4076(92)90038-S   [Google Scholar]
  15. Greene WH (1990). A gamma-distributed stochastic frontier model. Journal of Econometrics, 46(1-2): 141-163. https://doi.org/10.1016/0304-4076(90)90052-U   [Google Scholar]
  16. Hamidi S (2016). Measuring efficiency of governmental hospitals in Palestine using stochastic frontier analysis. Cost Effectiveness and Resource Allocation, 14: 3. https://doi.org/10.1186/s12962-016-0052-5   [Google Scholar] PMid:26848283 PMCid:PMC4741008
  17. Hasan MZ, Kamil AA, Mustafa A, and Baten MA (2012). Stochastic frontier model approach for measuring stock market efficiency with different distributions. PloS one, 7(5): e37047. https://doi.org/10.1371/journal.pone.0037047   [Google Scholar] PMid:22629352 PMCid:PMC3355172
  18. Iliyasu A, Mohamed ZA, Ismail MM, Amin AM, and Mazuki H (2016). Technical efficiency of cage fish farming in Peninsular Malaysia: A stochastic frontier production approach. Aquaculture Research, 47(1): 101-113. https://doi.org/10.1111/are.12474   [Google Scholar]
  19. Jansen IP, Ramnath S, and Yohn TL (2008). Changes in asset turnover and profit margin as signals of earnings management. Working Paper Series WCRFS: 08-09, Whitcomb Center for Research in Financial Services, New Jersey, USA.   [Google Scholar]
  20. Jansen IP, Ramnath S, and Yohn TL (2012). A diagnostic for earnings management using changes in asset turnover and profit margin. Contemporary Accounting Research, 29(1): 221-251. https://doi.org/10.1111/j.1911-3846.2011.01093.x   [Google Scholar]
  21. Jondrow J, Lovell CK, Materov IS, and Schmidt P (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19(2-3): 233-238. https://doi.org/10.1016/0304-4076(82)90004-5   [Google Scholar]
  22. Kazukauskas A, Newman CF, and Thorne FS (2010). Analysing the effect of decoupling on agricultural production: Evidence from Irish dairy farms using the Olley and Pakes approach. German Journal of Agricultural Economics, 59: 144-157. http://dx.doi.org/10.22004/ag.econ.145290   [Google Scholar]
  23. Kirkley JE, Squires D, and Strand IE (1995). Assessing technical efficiency in commercial fisheries: The mid-Atlantic sea scallop fishery. American Journal of Agricultural Economics, 77(3): 686-697. https://doi.org/10.2307/1243235   [Google Scholar]
  24. Meeusen W and Broeck VDJ (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2): 435-444. https://doi.org/10.2307/2525757   [Google Scholar]
  25. Mokhtar M, Shuib A, and Mohamad D (2014). Identifying the critical financial ratios for stocks evaluation: A fuzzy Delphi approach. In Aip Conference Proceedings 1635: 348. https://doi.org/10.1063/1.4903606   [Google Scholar]
  26. Reinganum MR (1983). Portfolio strategies based on market capitalization. Ariel, 134: 60-119.   [Google Scholar]
  27. Ritter C and Simar L (1997). Pitfalls of normal-gamma stochastic frontier models. Journal of Productivity Analysis, 8(2): 167-182. https://doi.org/10.1023/A:1007751524050   [Google Scholar]
  28. Rosko MD and Mutter RL (2008). Stochastic frontier analysis of hospital inefficiency: A review of empirical issues and an assessment of robustness. Medical Care Research and Review, 65(2): 131-166. https://doi.org/10.1177/1077558707307580   [Google Scholar] PMid:18045984
  29. Ruggiero J (1999). Efficiency estimation and error decomposition in the stochastic frontier model: A Monte Carlo analysis. European Journal of Operational Research, 115(3): 555-563. https://doi.org/10.1016/S0377-2217(98)00245-8   [Google Scholar]
  30. Sheela SC and Karthikeyan K (2012). Financial performance of pharmaceutical industry in India using DuPont analysis. European Journal of Business and Management, 4(14): 84-91.   [Google Scholar]
  31. Silva TC, Tabak BM, Cajueiro DO, and Dias MVB (2017). A comparison of DEA and SFA using micro-and macro-level perspectives: Efficiency of Chinese local banks. Physica A: Statistical Mechanics and its Applications, 469: 216-223. https://doi.org/10.1016/j.physa.2016.11.041   [Google Scholar]
  32. Soliman MT (2004). Using industry-adjusted DuPont analysis to predict future profitability. https://doi.org/10.2139/ssrn.456700   [Google Scholar]
  33. Soliman MT (2008). The use of DuPont analysis by market participants. The Accounting Review, 83(3): 823-853. https://doi.org/10.2308/accr.2008.83.3.823   [Google Scholar]
  34. Stevenson RE (1980). Likelihood functions for generalized stochastic frontier estimation. Journal of Econometrics, 13(1): 57-66. https://doi.org/10.1016/0304-4076(80)90042-1   [Google Scholar]
  35. Webster R, Kennedy SK, and Johnson L (1998). Comparing techniques for measuring the efficiency and productivity of Australian private hospitals. Working Papers in Econometrics and Applied Statistics No. 98/3, Australian Bureau of Statistics, Canberra, Australia.   [Google Scholar]
  36. Yakob R and Isa Z (2008). Kesolvenan dan kecekapan teknikal syarikat insurans hayat di Malaysia. International Journal of Management Studies (IJMS), 15: 177-197.   [Google Scholar]
  37. Yane S and Berg S (2013). Sensitivity analysis of efficiency rankings to distributional assumptions: Applications to Japanese water utilities. Applied Economics, 45(17): 2337-2348. https://doi.org/10.1080/00036846.2012.663475   [Google Scholar]
  38. Yang HH (2010). Measuring the efficiencies of Asia–Pacific international airports–Parametric and non-parametric evidence. Computers and Industrial Engineering, 59(4): 697-702. https://doi.org/10.1016/j.cie.2010.07.023   [Google Scholar]
  39. Zhou P, Ang BW, and Zhou DQ (2012). Measuring economy-wide energy efficiency performance: A parametric frontier approach. Applied Energy, 90(1): 196-200. https://doi.org/10.1016/j.apenergy.2011.02.025   [Google Scholar]