Bøger af Shawna Grosskopf

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.

The basic notion underlying this monograph - budget or revenue constrained models of production - we owe to Ronald W. Our contributions include an expanded set of duality results and a general bent toward empirical implementation: including various parameterizations, applications to efficiency and productivity measurement, and shadow pricing.

As you shall see, the directional distance function includes traditional distance functions and efficiency measures as special cases providing a unifying framework for existing productivity and efficiency measures.

This work applies efficiency literature to the case of intertemporal models. Topics covered include: static network models which serve as building blocks for the intertemporal budgeting models and the dynamic models and productivity measurements.