A new stepwise method for selection of input and output variables in data envelopment analysis

Abstract

Data envelopment analysis (DEA) is one of the widely accepted optimization technique uses to measure the relative efficiency of organizational units where multiple inputs and outputs are present. The significance of DEA results depends on the variables selected for DEA modelling. One of the main challenges in data envelopment analysis modelling is of identify the significant input and output variables for DEA modelling. In this study, we propose an enhanced stepwise method to identify the significant and insignificant input and output variable by reducing the iterations process in stepwise method. The statistical significance of the input and output variables evaluated using the statistical methods: Least significance difference (LSD), and Welch’s statistics. The proposed method applied to the Indian banking sector and the results have shown that the proposed model significantly identified the significant and insignificant input and output variables with least loss of information.