• 刀根 薫政策研究大学院大学

In this paper, we propose new resampling models in data envelopment analysis (DEA). Input/output values are subject to change for several reasons, e.g., measurement errors, hysteretic factors, arbitrariness and so on. Furthermore, these variations differ in their input/output items and their decision-making units (DMU). Hence, DEA efficiency scores need to be examined by considering these factors. Resampling based on these variations is necessary for gauging the confidence interval of DEA scores. We propose three resampling models. The first one assumes downside and upside measurement error rates for each input/output, which are common to all DMUs. We resample data following the triangular distribution that the downside and upside errors indicate around the observed data. The second model utilizes historical data, e.g., past-present, for estimating data variations, imposing chronological order weights which are supplied by Lucas series (a variant of Fibonacci series). The last one deals with future prospects. This model aims at forecasting the future efficiency score and its confidence interval for each DMU.