Merger analysis using inverse DEA: the case of variable returns to scale
محورهای موضوعی : Data Envelopment Analysis
1 - Faculty of Industrial Engineering and Management Science, Shahrood University of Technology
کلید واژه: Inverse DEA, Merger Analysis, Semi-additive technology, financial institute,
چکیده مقاله :
In a dynamic economy, mergers and consolidations of economic and finance sectors likebanks, etc. becoming more common. One of the applications of the inverseDEA is the merger analysis of a series of production units. Data envelopment analysis (DEA) measures the efficiency score of a decision making unit (DMU) considering its input and output level. On the other side, the inverse DEA approach aims to find required input levels for DMU to produce a presumed output level preserving the efficiency score. In a rather recent paper, (Gattoufi, Amin, & Emrouznejad, 2014) introduced an interesting application of inverse DEA models for merger analysis. The current paper extends this work by developing a generalized inverse DEA model assuming variable returns to scale. In contrast with (Gattoufi et al., 2014), it is shown that proposed models are always feasible and bounded. The idea is illustrated using a methodical argument and two numerical examples. An empirical study of financial institutes shows the strength and the applicability of the proposed methods.
In a dynamic economy, mergers and consolidations of economic and finance sectors likebanks, etc. becoming more common. One of the applications of the inverseDEA is the merger analysis of a series of production units. Data envelopment analysis (DEA) measures the efficiency score of a decision making unit (DMU) considering its input and output level. On the other side, the inverse DEA approach aims to find required input levels for DMU to produce a presumed output level preserving the efficiency score. In a rather recent paper, (Gattoufi, Amin, & Emrouznejad, 2014) introduced an interesting application of inverse DEA models for merger analysis. The current paper extends this work by developing a generalized inverse DEA model assuming variable returns to scale. In contrast with (Gattoufi et al., 2014), it is shown that proposed models are always feasible and bounded. The idea is illustrated using a methodical argument and two numerical examples. An empirical study of financial institutes shows the strength and the applicability of the proposed methods.
