Analyzing the accuracy of variable returns to scale data envelopment analysis models

The data envelopment analysis (DEA) model is extensively used to estimate efficiency, but no study has determined the DEA that delivers most precise estimates. To address this issue, we advance Monte Carlo simulation-based generation process proposed by Kohl and Brunner (2020). developed generates an artificial dataset using Translog production function (instead of commonly Cobb Douglas) construct well-behaved scenarios under variable returns scale (VRS). Using different VRS models, compute efficiency scores with artificially generated decision-making units (DMUs). We employ five performance indicators followed a benchmark value ranking as well statistical hypothesis tests evaluate quality procedure allows us determine which parameters negatively or positively influence It also enables identify performs efficiently over wide range scenarios. In contrast widely applied BCC (Banker-Charnes-Cooper) model, find Assurance Region (AR) Slacks-Based Measurement (SBM) models perform better. Thus, endorse use AR SBM for applications regime.