Efficiency and Productivity Analysis: Using Copulas in Stochastic Frontier Models

This book is an easy-to-understand guide to modeling productivity and efficiency using modern statistical tools. It introduces readers to the fundamentals of stochastic frontier analysis (SFA) and gradually takes them to the forefront of academic research in this area, examining the latest concepts and methods related to the use of copulas in SFA.

Following a comprehensive review of classic methodology, Professor Artem Prokhorov covers topics in panel data modeling, in endogeneity in SFA, in joint modeling of technical and allocative inefficiency, and in optimal and robust prediction of inefficiency scores. This is done using copulas to capture various kinds of statistical dependence that have been previously ignored when modeling production. The classic and advanced topics are illustrated using practical examples and codes written in modern programming languages. As an important example, the book spells out the case where both the values of output and the ratio of inputs used in production are optimized simultaneously. Such simultaneity, if ignored, leads to biased estimates of productivity and returns-to-scale and may understate inefficiency.

The book offers a useful reference for those interested in the newest robust methods of business analytics in the area of productivity and efficiency, with implications for strategy, budgeting, resourcing and benchmarking of firms, industries and production units more generally.

1144052014
Efficiency and Productivity Analysis: Using Copulas in Stochastic Frontier Models

This book is an easy-to-understand guide to modeling productivity and efficiency using modern statistical tools. It introduces readers to the fundamentals of stochastic frontier analysis (SFA) and gradually takes them to the forefront of academic research in this area, examining the latest concepts and methods related to the use of copulas in SFA.

Following a comprehensive review of classic methodology, Professor Artem Prokhorov covers topics in panel data modeling, in endogeneity in SFA, in joint modeling of technical and allocative inefficiency, and in optimal and robust prediction of inefficiency scores. This is done using copulas to capture various kinds of statistical dependence that have been previously ignored when modeling production. The classic and advanced topics are illustrated using practical examples and codes written in modern programming languages. As an important example, the book spells out the case where both the values of output and the ratio of inputs used in production are optimized simultaneously. Such simultaneity, if ignored, leads to biased estimates of productivity and returns-to-scale and may understate inefficiency.

The book offers a useful reference for those interested in the newest robust methods of business analytics in the area of productivity and efficiency, with implications for strategy, budgeting, resourcing and benchmarking of firms, industries and production units more generally.

49.99 In Stock
Efficiency and Productivity Analysis: Using Copulas in Stochastic Frontier Models

Efficiency and Productivity Analysis: Using Copulas in Stochastic Frontier Models

by Artem Prokhorov
Efficiency and Productivity Analysis: Using Copulas in Stochastic Frontier Models

Efficiency and Productivity Analysis: Using Copulas in Stochastic Frontier Models

by Artem Prokhorov

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$49.99 

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Overview

This book is an easy-to-understand guide to modeling productivity and efficiency using modern statistical tools. It introduces readers to the fundamentals of stochastic frontier analysis (SFA) and gradually takes them to the forefront of academic research in this area, examining the latest concepts and methods related to the use of copulas in SFA.

Following a comprehensive review of classic methodology, Professor Artem Prokhorov covers topics in panel data modeling, in endogeneity in SFA, in joint modeling of technical and allocative inefficiency, and in optimal and robust prediction of inefficiency scores. This is done using copulas to capture various kinds of statistical dependence that have been previously ignored when modeling production. The classic and advanced topics are illustrated using practical examples and codes written in modern programming languages. As an important example, the book spells out the case where both the values of output and the ratio of inputs used in production are optimized simultaneously. Such simultaneity, if ignored, leads to biased estimates of productivity and returns-to-scale and may understate inefficiency.

The book offers a useful reference for those interested in the newest robust methods of business analytics in the area of productivity and efficiency, with implications for strategy, budgeting, resourcing and benchmarking of firms, industries and production units more generally.


Product Details

ISBN-13: 9781003855217
Publisher: Taylor & Francis
Publication date: 02/28/2024
Series: Routledge Advanced Texts in Economics and Finance
Sold by: Barnes & Noble
Format: eBook
Pages: 244
File size: 3 MB

About the Author

Artem Prokhorov is a Professor at the Business School of the University of Sydney, Australia. He is also a Research Fellow at the Centre Interuniversitaire de Recherche en Économie Quantitative (CIREQ) at the University of Montreal, Canada, and a Co-Founder and Academic Advisor of the Center for Econometrics and Business Analytics (CEBA) at St. Petersburg State University, Russia. Professor Prokhorov is a founding member of the International Society for Efficiency and Productivity Analysis (ISEAPA). He holds a Cand Sci degree from St. Petersburg State University and a PhD degree in Economics from Michigan State University.

Table of Contents

1. Introduction 2. Basics of Stochastic Frontier Analysis for Cross-Sectional Data 3. Basics of Stochastic Frontier Analysis for Panel Data 4. Copulas for Panel Stochastic Frontier Models 5. Modeling Endogeneity in Stochastic Frontier Models 6. Modeling Dependence in Production Systems 7. Predicting Inefficiency 8. Concluding Remarks

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