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

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