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).
Written by production economics and finance specialists Rolf Färe and Shawna Grosskopf of Oregon State University and Dimitris Margaritis of the University of Auckland, Pricing Non-marketed Goods Using Distance Functions, is an inspiring new contribution highlighting the importance of duality theory for valuation purposes, especially for hard to price inputs or resources, intended or unintended goods and assets. The theoretical pricing models are supplemented by self-standing empirical applications covering real estate pricing, environmental preservation, transfer pricing, shadow prices of university knowledge outputs and spillovers, and the pricing of bank equity capital and non-performing loans.
Written by production economics and finance specialists Rolf Färe and Shawna Grosskopf of Oregon State University and Dimitris Margaritis of the University of Auckland, Pricing Non-marketed Goods Using Distance Functions, is an inspiring new contribution highlighting the importance of duality theory for valuation purposes, especially for hard to price inputs or resources, intended or unintended goods and assets. The theoretical pricing models are supplemented by self-standing empirical applications covering real estate pricing, environmental preservation, transfer pricing, shadow prices of university knowledge outputs and spillovers, and the pricing of bank equity capital and non-performing loans.
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).
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