Using regression and Data Envelopment Analysis (DEA) to forecast bank performance over time

Abstract

Forecasting is an important tool used by businesses to plan and evaluate their operations. One of the most commonly used techniques for forecasting is regression analysis. Often forecasts are produced based upon a set of comparable units which could be individuals, groups, departments or companies that perform similar activities such as a set of banks, a group of mangers and so on. We apply a methodology that includes a new independent variable, the comparable unit's DEA relative efficiency, into the regression analysis. The results of applying this methodology to the performance of commercial banks over time will be presented.

Determinantes Da Inovação Em Micro E Pequenas Empresas: Uma Abordagem Gerencial

“…For DEA, we considered the significant variables in the regression, since the wrong specification of the model may affect the quality of the results (Klimberg et al, 2009). The analysis of the decisionmaking units showed that the sample had an average efficiency of 0.86, in which 174 companies were considered efficient and 141 companies were not efficient regarding innovation capability.…”

“…Although regression represents a good method for analysis, derived models can provide misguided prediction measures (Klimberg, Lawrence, Yermish, La, & Mrazik, 2009), since the relative weight of independent variables can vary between comparable units.…”

Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals

“…It is important to note some advantages of DEA-CCR over RA being as follows: DEA-CCR measures performance against efficient rather than average performance, DEA-CCR offers more accurate estimates of relative efficiency because it is a boundary method; DEA-CCR normally yields more accurate targets because it is a boundary method, and so on. However, some advantages of RA over DEA-CCR may be summarised as: RA offers a better predictor of future performance at the collective DMU level if it is assumed inefficiencies cannot be eliminated, RA offers the facility to estimate confidence intervals for point estimates, and RA could yield better estimates of individual maximum (minimum) levels where outputs (inputs) can vary independently of one another, among others [17] to [20].…”

“…Regression analysis (RA) is primarily applied to analyse the relationship between one dependent variable and several independent variables. Here, we apply and extend some of the papers in which authors introduced a methodology that incorporates a new variable into the regression analysis that captures the unique weighting of each comparable unit, [17] to [20]. In [17], the different models (DEA and RA) were used in various combinations to determine efficiency estimation and evaluation.…”

“…In [19], the major differences between RA and DEA were identified, and their appropriateness as a primary means for assessing relative efficiency in the context of regulation with an application to the water industry were evaluated. A methodology that includes a new independent variable, the comparable unit's DEA relative efficiency, into the RA is applied in [20].…”