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A key resource and framework for assessing the performance of competing entities, including forecasting models
Advances in DEA Theory and Applications provides a much-needed framework for assessing the performance of competing entities with special emphasis on forecasting models. It helps readers to determine the most appropriate methodology in order to make the most accurate decisions for implementation. Written by a noted expert in the field, this text provides a review of the latest advances in DEA theory and applications to the field of forecasting.
Designed for use by anyone involved in research in the field of forecasting or in another application area where forecasting drives decision making, this text can be applied to a wide range of contexts, including education, health care, banking, armed forces, auditing, market research, retail outlets, organizational effectiveness, transportation, public housing, and manufacturing. This vital resource:
* Explores the latest developments in DEA frameworks for the performance evaluation of entities such as public or private organizational branches or departments, economic sectors, technologies, and stocks
* Presents a novel area of application for DEA; namely, the performance evaluation of forecasting models
* Promotes the use of DEA to assess the performance of forecasting models in a wide area of applications
* Provides rich, detailed examples and case studies
Advances in DEA Theory and Applications includes information on a balanced benchmarking tool that is designed to help organizations examine their assumptions about their productivity and performance.
Sommario
LIST OF CONTRIBUTORS xx
ABOUT THE AUTHORS xxii
PREFACE xxxii
PART I DEA THEORY 1
1 Radial DEA Models 3
Kaoru Tone
1.1 Introduction 3
1.2 Basic Data 3
1.3 Input-Oriented CCR Model 4
1.4 The Input-Oriented BCC Model 6
1.5 The Output-Oriented Model 7
1.6 Assurance Region Method 8
1.7 The Assumptions Behind Radial Models 8
1.8 A Sample Radial Model 8
References 10
2 Non-Radial DEA Models 11
Kaoru Tone
2.1 Introduction 11
2.2 The SBM Model 12
2.3 An Example of an SBM Model 15
2.4 The Dual Program of the SBM Model 17
2.5 Extensions of the SBM Model 17
2.6 Concluding Remarks 18
References 19
3 Directional Distance DEA Models 20
Hirofumi Fukuyama and William L. Weber
3.1 Introduction 20
3.2 Directional Distance Model 20
3.3 Variable-Returns-to-Scale DD Models 23
3.4 Slacks-Based DD Model 23
3.5 Choice of Directional Vectors 25
References 26
4 Super-Efficiency DEA Models 28
Kaoru Tone
4.1 Introduction 28
4.2 Radial Super-Efficiency Models 28
4.3 Non-Radial Super-Efficiency Models 29
4.4 An Example of a Super-Efficiency Model 31
References 32
5 Determining Returns to Scale in the VRS DEA Model 33
Biresh K. Sahoo and Kaoru Tone
5.1 Introduction 33
5.2 Technology Specification and Scale Elasticity 34
5.3 Summary 37
References 37
6 Malmquist Productivity Index Models 40
Kaoru Tone and Miki Tsutsui
6.1 Introduction 40
6.2 Radial Malmquist Model 43
6.3 Non-Radial and Oriented Malmquist Model 45
6.4 Non-Radial and Non-Oriented Malmquist Model 47
6.5 Cumulative Malmquist Index (CMI) 48
6.6 Adjusted Malmquist Index (AMI) 49
6.7 Numerical Example 50
6.8 Concluding Remarks 55
References 55
7 The Network DEA Model 57
Kaoru Tone and Miki Tsutsui
7.1 Introduction 57
7.2 Notation and Production Possibility Set 58
7.3 Description of Network Structure 59
7.4 Objective Functions and Efficiencies 61
Reference 63
8 The Dynamic DEA Model 64
Kaoru Tone and Miki Tsutsui
8.1 Introduction 64
8.2 Notation and Production Possibility Set 65
8.3 Description of Dynamic Structure 67
8.4 Objective Functions and Efficiencies 69
8.5 Dynamic Malmquist Index 71
References 73
9 The Dynamic Network DEA Model 74
Kaoru Tone and Miki Tsutsui
9.1 Introduction 74
9.2 Notation and Production Possibility Set 75
9.3 Description of Dynamic Network Structure 77
9.4 Objective Function and Efficiencies 80
9.5 Dynamic Divisional Malmquist Index 82
References 84
10 Stochastic DEA: The Regression-Based Approach 85
Andrew L. Johnson
10.1 Introduction 85
10.2 Review of Literature on Stochastic DEA 87
10.3 Conclusions 96
References 96
11 A Comparative Study of AHP and DEA 100
Kaoru Tone
11.1 Introduction 100
11.2 A Glimpse of Data Envelopment Analysis 100
11.3 Benefit/Cost Analysis by Analytic Hierarchy Process 102
11.4 Efficiencies in AHP and DEA 104
11.5 Concluding Remarks 105
References 106
12 A Computational Method for Solving DEA Problems with Infinitely Many DMUs 107
Abraham Charnes and Kaoru Tone
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KAORU TONE is with the National Graduate Institute for Policy Studies, Japan. His contribution to DEA has a variety of attainments. He authored a classical book
Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software under the co-authorship with Professor Cooper (University of Texas) and Professor Seiford (University of Michigan). He also published many papers on DEA in international journals. Kaoru Tone opened a new avenue for performance evaluation, called Slacks-based Measure (SBM) that is widely utilized over the world. His recent innovations include Network SBM, Dynamic SBM, Dynamic DEA with Network Structure, Congestion, Returns-to-growth in DEA, Ownership-specified Network DEA, Non-convex Frontier DEA, Past-Present-Future Inter-temporal DEA, Resampling DEA and SBM-Max.
Riassunto
A key resource and framework for assessing the performance of competing entities, including forecasting models Advances in DEA Theory and Applications provides a much-needed framework for assessing the performance of competing entities with special emphasis on forecasting models.
