Evaluation and prediction on total factor productivity of Chinese petroleum companies via three-stage DEA model and time series neural network model

Abstract

In this paper, the panel data of 50 listed Chinese petroleum companies from 2009 to 2018 were used. A three-stage Data Envelopment Analysis (DEA) model was used to eliminate environmental factors and random factors, Pearson Epsilon-based Measure (PEBM) distance function and the Malmquist index were combined to evaluate the change of total factor productivity, then Support Vector Regression (SVR) prediction model and Radial Basis Function (RBF) neural network prediction model were compared and it was found that the latter had a small error of prediction on Total Factor Productivity (TFP), hence the radial basis function neural network prediction model was used to predict the total factor productivity of listed petroleum companies in the next two years. The study showed that the average annual growth rate of total factor productivity of the listed petroleum companies was 9.05 %, and its Efficiency Change (EC) index and Scale Efficiency Change (SEC) index were the main driving force for the growth of total factor productivity, while its Technical Change (TC) index and magnitude technical change (MATC) index had an inhibitory effect on the total factor productivity. And the predicted results showed that total factor productivity of the petroleum companies will continue to grow in the next two years. In addition, the research methods provided in this paper can be used not only to evaluate and predict total factor productivity of petroleum field, but also to evaluate and predict total factor productivity of other fields.