Advances In Data Envelopment Analysis

Data Envelopment Analysis (DEA) is often overlooked in empirical work such as diagnostic tests to determine whether the data conform with technology which, in turn, is important in identifying technical change, or finding which types of DEA models allow data transformations, including dealing with ordinal data.

Advances in Data Envelopment Analysis focuses on both theoretical developments and their applications into the measurement of productive efficiency and productivity growth, such as its application to the modelling of time substitution, i.e. the problem of how to allocate resources over time, and estimating the "value" of a Decision Making Unit (DMU).

Contents: Acknowledgements Preface Introduction: The DEA Technology and Its Representation (Axiomatic) Properties of the DEA Model Appendix Looking at the Data in DEA: Data Diagnostics Technical Change Data Translation Appendix: Distance Functions DEA and Intensity Variables: On Shephard's Duality Theory Adjoint Transformations in DEA The Diet Problem Pricing Decision Making Units DEA and Directional Distance Functions: Directional Vectors Aggregation and Directional Vectors Endogenizing the Directional Vector Appendix DEA and Time Substitution: Theoretical Underpinning Reassessing the EU Stability and Growth Pact Method Some Limitations of Two DEA Models: The Non-Archimedean and DEA Super-Efficiency and Zeros ReferencesAdvanced postgraduate students and researchers in operations research and economics with a particular interest in production theory and operations management.