Forecasting DEA scores

The last decade has seen significant work examining the statistical properties of the measurements yielded by Data Envelopment Analysis (DEA). However, no attempt has been made to predict DEA scores for the individual decision-making units (DMUs) being analyzed. This paper develops an autoregressive model using the Box-Jenkins methodology to predict and tests it on data sets from three different industries—Canadian banking, U.S. major oil companies, and Japanese power producers. In the process, a novel measure for DMU comparison is developed and a new density function is introduced to show “distances” between inefficient DMus. The model is shown to provide good in-sample fits and out-of-sample predictions for all three industries.