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This book provides a detailed introduction to the theoretical and
methodological foundations of production efficiency analysis using
benchmarking. Two of the more popular methods of efficiency
evaluation are Stochastic Frontier Analysis (SFA) and Data
Envelopment Analysis (DEA), both of which are based on the concept
of a production possibility set and its frontier. Depending on the
assumed objectives of the decision-making unit, a Production, Cost,
or Profit Frontier is constructed from observed data on input and
output quantities and prices. While SFA uses different maximum
likelihood estimation techniques to estimate a parametric frontier,
DEA relies on mathematical programming to create a nonparametric
frontier. Yet another alternative is the Convex Nonparametric
Frontier, which is based on the assumed convexity of the production
possibility set and creates a piecewise linear frontier consisting
of a number of tangent hyper planes. Three of the papers in this
volume provide a detailed and relatively easy to follow exposition
of the underlying theory from neoclassical production economics and
offer step-by-step instructions on the appropriate model to apply
in different contexts and how to implement them. Of particular
appeal are the instructions on (i) how to write the codes for
different SFA models on STATA, (ii) how to write a VBA Macro for
repetitive solution of the DEA problem for each production unit on
Excel Solver, and (iii) how to write the codes for the
Nonparametric Convex Frontier estimation. The three other papers in
the volume are primarily theoretical and will be of interest to PhD
students and researchers hoping to make methodological and
conceptual contributions to the field of nonparametric efficiency
analysis.
This book provides a detailed introduction to the theoretical and
methodological foundations of production efficiency analysis using
benchmarking. Two of the more popular methods of efficiency
evaluation are Stochastic Frontier Analysis (SFA) and Data
Envelopment Analysis (DEA), both of which are based on the concept
of a production possibility set and its frontier. Depending on the
assumed objectives of the decision-making unit, a Production, Cost,
or Profit Frontier is constructed from observed data on input and
output quantities and prices. While SFA uses different maximum
likelihood estimation techniques to estimate a parametric frontier,
DEA relies on mathematical programming to create a nonparametric
frontier. Yet another alternative is the Convex Nonparametric
Frontier, which is based on the assumed convexity of the production
possibility set and creates a piecewise linear frontier consisting
of a number of tangent hyper planes. Three of the papers in this
volume provide a detailed and relatively easy to follow exposition
of the underlying theory from neoclassical production economics and
offer step-by-step instructions on the appropriate model to apply
in different contexts and how to implement them. Of particular
appeal are the instructions on (i) how to write the codes for
different SFA models on STATA, (ii) how to write a VBA Macro for
repetitive solution of the DEA problem for each production unit on
Excel Solver, and (iii) how to write the codes for the
Nonparametric Convex Frontier estimation. The three other papers in
the volume are primarily theoretical and will be of interest to PhD
students and researchers hoping to make methodological and
conceptual contributions to the field of nonparametric efficiency
analysis.
Using the neo-classical theory of production economics as the
analytical framework, this book, first published in 2004, provides
a unified and easily comprehensible, yet fairly rigorous,
exposition of the core literature on data envelopment analysis
(DEA) for readers based in different disciplines. The various DEA
models are developed as nonparametric alternatives to the
econometric models. Apart from the standard fare consisting of the
basic input- and output-oriented DEA models formulated by Charnes,
Cooper, and Rhodes, and Banker, Charnes, and Cooper, the book
covers developments such as the directional distance function, free
disposal hull (FDH) analysis, non-radial measures of efficiency,
multiplier bounds, mergers and break-up of firms, and measurement
of productivity change through the Malmquist total factor
productivity index. The chapter on efficiency measurement using
market prices provides the critical link between DEA and the
neo-classical theory of a competitive firm. The book also covers
several forms of stochastic DEA in detail.
Using the neo-classical theory of production economics as the
analytical framework, this book, first published in 2004, provides
a unified and easily comprehensible, yet fairly rigorous,
exposition of the core literature on data envelopment analysis
(DEA) for readers based in different disciplines. The various DEA
models are developed as nonparametric alternatives to the
econometric models. Apart from the standard fare consisting of the
basic input- and output-oriented DEA models formulated by Charnes,
Cooper, and Rhodes, and Banker, Charnes, and Cooper, the book
covers developments such as the directional distance function, free
disposal hull (FDH) analysis, non-radial measures of efficiency,
multiplier bounds, mergers and break-up of firms, and measurement
of productivity change through the Malmquist total factor
productivity index. The chapter on efficiency measurement using
market prices provides the critical link between DEA and the
neo-classical theory of a competitive firm. The book also covers
several forms of stochastic DEA in detail.
This two-volume handbook includes state-of-the-art surveys in
different areas of neoclassical production economics. Volume 1
covers theoretical and methodological issues only. Volume 2
includes surveys of empirical applications in different areas like
manufacturing, agriculture, banking, energy and environment, and so
forth.
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