Efficiency Predictions by Fuzzy Piecewise Auto-regression
Journal
資訊管理學報
Journal Volume
17
Pages
197-220
Date Issued
2010
Author(s)
Abstract
Predicting productivity and efficiency during the transfer of input to output is a key issue in many manufacturing and service operation applications. Operation research and econometrics acknowledge that efficiency analysis is a major research issue. Data envelopment analysis (DEA) has substantially reshaped the result of information economics in previous years. However, the extent to which asymmetric information is relevant for efficiency prediction has rarely been sought empirically. Efficiency prediction plays a crucial role in many state-of-the-art applications and planning. Forecasting methodologies that can accurately predict efficiency scores can help in strategic decision-making. This study focuses on fuzzy piecewise auto-regression and the catching-up efficiency index (CIE), which supports efficiency prediction. In this study, two regression models were formulated by utilizing data from commercial banks in Taiwan from 2002 to 2005. These models were used to validate banking efficiency scores for 2005 and 2006, and to predict the banking efficiency scores for 2007. The results of a thorough computational analysis provide a range indicating the prediction value for each bank based on fuzzy regression characteristics.
SDGs
Type
journal article