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E-raamat: Practitioner's Guide to Stochastic Frontier Analysis Using Stata

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(Binghamton University, State University of New York), (National Taiwan University),
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  • Ilmumisaeg: 26-Jan-2015
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781316190821
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Practitioner's Guide to Stochastic Frontier Analysis Using StataSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 26-Jan-2015
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781316190821
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"A Practitioner's Guide to Stochastic Frontier Analysis Using Stata provides practitioners in academia and industry with a step-by-step guide on how to conduct efficiency analysis using the stochastic frontier approach. The authors explain in detail how to estimate production, cost, and profit efficiency and introduce the basic theory of each model in an accessible way, using empirical examples that demonstrate the interpretation and application of models. This book also provides computer code, allowing users to apply the models in their own work, and incorporates the most recent stochastic frontier models developed in academic literature. Such recent developments include models of heteroscedasticity and exogenous determinants of inefficiency, scaling models, panel models with time-varying inefficiency, growth models, and panel models that separate firm effects and persistent and transient inefficiency. Immensely helpful to applied researchers, this book bridges the chasm between theory and practice, expanding the range of applications in which production frontier analysis may be implemented"--

"tochastic Frontier Analysis Using Stata provides practitioners in academia and industry with a step-by-step guide on how to conduct efficiency analysis using the stochastic frontier approach. The authors explain in detail how to estimate production, cost, and profit efficiency and introduce the basic theory of each model in an accessible way, using empirical examples that demonstrate the interpretation and application of models. This book also provides computer code, allowing users to apply the models in their own work, and incorporates the most recent stochastic frontier models developed in academic literature. Such recent developments include models of heteroscedasticity and exogenous determinants of inefficiency, scaling models, panel models with time-varying inefficiency, growth models, and panel models that separate firm effects and persistent and transient inefficiency. Immensely helpful to applied researchers, this book bridges the chasm between theory and practice, expanding the range of applications in which production frontier analysis may be implemented"--

Arvustused

'A competent empirical application of Stochastic Frontier Analysis (SFA) requires a clear understanding of both the production economics and the econometric theory behind the specified model side by side with adequate programming skills to write the necessary software codes. Apart from a clear exposition of the economic theory behind various stochastic frontier models that represent the technology (like the Distance Functions) and/or producer behavior (like the Cost or Profit Functions) and the relevant econometric theory, the authors offer detailed instructions on how to write the commands for various models in Stata and explain how to interpret the results. This book will prove to be invaluable for every serious researcher using SFA to measure production efficiency.' Subhash C. Ray, University of Connecticut 'This book is a significant contribution to an applied introduction to stochastic frontier analysis. The authors explain clearly many of the models used in efficiency estimation, which has become a standard tool in the arsenal of applied economics. They explain clearly the models and the assumptions and provide a thorough introduction to estimating performance and efficiency for the practitioner. The many scientific fields in which efficiency and performance measurement are important will benefit immensely from the book not only because of its clarity and concreteness but also because the models are taken directly to practice using Stata, standard software used by many researchers. The combination of theory and practical application is masterfully done in this book, and practitioners in a vast number of fields will find it indispensable for their research.' Mike G. Tsionas, Athens University of Economics and Business

Muu info

This book provides practitioners with a step-by-step guide on how to conduct efficiency analysis using the stochastic frontier approach.
Preface xiii
Part I General Information
1 Introduction
3(6)
1.1 What This Book Is About
3(1)
1.2 Who Should Read This Book?
4(1)
1.3 The Structure of This Book
5(4)
2 Production, Distance, Cost, and Profit Functions
9(38)
2.1 Introduction
9(1)
2.2 The Production Function and Technical Efficiency
10(5)
2.2.1 Input-Oriented and Output-Oriented Technical Inefficiency
12(3)
2.2.2 Non-Neutral Technical Inefficiency
15(1)
2.3 Statistics from Production Functions
15(4)
2.3.1 Homogeneity and Returns to Scale
16(1)
2.3.2 Substitutability
17(1)
2.3.3 Separabilitiy
17(1)
2.3.4 Technical Change
18(1)
2.4 Transformation of Production Functions
19(1)
2.5 Functional Forms of Production Functions
20(5)
2.5.1 The Cobb-Douglas (CD) Production Function
20(2)
2.5.2 The Generalized Production Function (GPF)
22(1)
2.5.3 The Transcendental Production Function
23(1)
2.5.4 The Translog Production Function
24(1)
2.6 Multiple Output Production Technology (Distance Functions)
25(6)
2.6.1 Distance Functions
27(3)
2.6.2 The Translog Input Distance Function
30(1)
2.6.3 The Translog Output Distance Function
30(1)
2.7 The Transformation Function Formulation
31(6)
2.7.1 The Transformation Function with Inefficiency
31(6)
2.8 Allocative Inefficiency
37(4)
2.8.1 Cost Minimization and Allocative Inefficiency
38(2)
2.8.2 Profit Maximization and Allocative Inefficiency
40(1)
2.9 The Indirect Production Function
41(6)
2.9.1 Modeling
41(6)
Part II Single Equation Approach: Production, Cost, And Profit
3 Estimation of Technical Efficiency in Production Frontier Models Using Cross-Sectional Data
47(53)
3.1 Introduction
47(1)
3.2 Output-Oriented Technical Efficiency
48(1)
3.3 Estimation Methods: Distribution-Free Approaches
49(6)
3.3.1 Corrected OLS (COLS)
50(3)
3.3.2 Corrected Mean Absolute Deviation (CMAD)
53(1)
3.3.3 Thick Frontier Approach
54(1)
3.4 Estimation Methods: Maximum Likelihood Estimators
55(40)
3.4.1 A Skewness Test on OLS Residuals
56(3)
3.4.2 Parametric Distributional Assumptions
59(1)
3.4.3 Half-Normal Distribution
59(14)
3.4.4 Truncated-Normal Distribution
73(12)
3.4.5 Truncated Distribution with the Scaling Property
85(5)
3.4.6 The Exponential Distribution
90(5)
3.5 Input-Oriented Technical Inefficiency
95(2)
3.6 Endogeneity and Input and Output Distance Functions
97(3)
4 Estimation of Technical Efficiency in Cost Frontier Models Using Cross-Sectional Data
100(28)
4.1 Introduction
100(1)
4.2 Input-Oriented Technical Inefficiency
101(7)
4.2.1 Price Homogeneity
103(2)
4.2.2 Monotonicity and Concavity
105(3)
4.3 Estimation Methods: Distribution-Free Approaches
108(7)
4.3.1 Corrected OLS
109(1)
4.3.2 Cases with No or Little Variation in Input Prices
110(1)
4.3.3 Thick Frontier Approach
110(3)
4.3.4 Quantile-Regression-Based TFA
113(2)
4.4 Estimation Methods: Maximum Likelihood Estimators
115(7)
4.4.1 Skewness Test on OLS Residuals
116(1)
4.4.2 The Half-Normal Distribution
117(3)
4.4.3 The Truncated-Normal, Scaling, and Exponential Models
120(2)
4.5 Output-Oriented Technical Inefficiency
122(6)
4.5.1 Quasi-Fixed Inputs
125(1)
4.5.2 Estimation Methods
125(3)
5 Estimation of Technical Efficiency in Profit Frontier Models Using Cross-Sectional Data
128(21)
5.1 Introduction
128(2)
5.2 Output-Oriented Technical Inefficiency
130(4)
5.3 Estimation Methods: Distribution-Free Approaches
134(2)
5.4 Estimation Methods: Maximum Likelihood Estimators
136(7)
5.5 Input-Oriented Technical Inefficiency
143(2)
5.6 Estimation Methods: Distribution-Free Approaches
145(1)
5.7 Estimation Methods: Maximum Likelihood Estimators
145(4)
Part III System Models With Cross-Sectional Data
6 Estimation of Technical Efficiency in Cost Frontier Models Using System Models with Cross-Sectional Data
149(24)
6.1 Introduction
149(1)
6.2 Single Output, Input-Oriented Technical Inefficiency
149(3)
6.3 Estimation Methods: Distribution-Free Approach
152(4)
6.4 Estimation Methods: Maximum Likelihood Estimators
156(13)
6.4.1 Heteroscedasticity, Marginal Effects, Efficiency Index, and Confidence Intervals
168(1)
6.5 Multiple Outputs, Input-Oriented Technical Inefficiency
169(2)
6.6 Estimation Methods
171(1)
6.7 Multiple Outputs, Output-Oriented Technical Inefficiency
171(2)
7 Estimation of Technical Efficiency in Profit Frontier Models Using System Models with Cross-Sectional Data
173(30)
7.1 Introduction
173(1)
7.2 Single Output, Output-Oriented Technical Inefficiency
173(3)
7.3 Estimation Methods: Distribution-Free Approaches
176(5)
7.4 Estimation Methods: System of Share Equations, Maximum Likelihood Estimators
181(8)
7.5 Estimation Methods: Imposing Homogeneity Assumptions, Maximum Likelihood Estimators
189(6)
7.6 Single Output, Input-Oriented Technical Inefficiency
195(1)
7.7 Multiple Output Technology
196(7)
7.7.1 Output-Oriented Technical Inefficiency
196(2)
7.7.2 Estimation Methods
198(5)
Part IV The Primal Approach
8 Estimation of Technical and Allocative Efficiency in Cost Frontier Models Using System Models with Cross-Sectional Data: A Primal System Approach
203(27)
8.1 Introduction
203(1)
8.2 Cost System Approach with Both Technical and Allocative Inefficiency
204(4)
8.3 The Primal System Approach with Technical and Allocative Inefficiency
208(2)
8.4 Estimation Methods When Algebraic Formula Can Be Derived
210(14)
8.4.1 The Cobb-Douglas Production Function
210(13)
8.4.2 The Generalized Production Function
223(1)
8.5 Estimation Methods When Algebraic Formula Cannot Be Derived
224(6)
8.5.1 Translog Production Function
224(6)
9 Estimation of Technical and Allocative Efficiency in Profit Frontier Models Using System Models with Cross-Sectional Data: A Primal System Approach
230(11)
9.1 Introduction
230(1)
9.2 The Profit Function Approach
231(1)
9.3 The Primal Approach of Profit Maximization with Both Technical and Allocative Inefficiency
231(2)
9.4 Estimation Methods: Maximum Likelihood Estimators
233(8)
9.4.1 Technical and Allocative Inefficiency Effect on Profit
236(5)
Part V Single Equation Approach With Panel Data
10 Estimation of Technical Efficiency in Single Equation Panel Models
241(38)
10.1 Introduction
241(2)
10.2 Time-Invariant Technical Inefficiency (Distribution-Free) Models
243(7)
10.2.1 The Fixed-Effects Model (Schmidt and Sickles [ 1984])
243(3)
10.2.2 The Random-Effects Model
246(4)
10.3 Time-Varying Technical Inefficiency Models
250(12)
10.3.1 Time-Varying Technical Inefficiency Models Using Distribution-Free Approaches
251(3)
10.3.2 Time-Varying Inefficiency Models with Deterministic and Stochastic Components
254(8)
10.4 Models That Separate Firm Heterogeneity from Inefficiency
262(8)
10.5 Models That Separate Persistent and Time-Varying Inefficiency
270(4)
10.5.1 The Fixed-Effects Model
271(1)
10.5.2 The Random-Effects Model
271(3)
10.6 Models That Separate Firm Effects, Persistent Inefficiency and Time-Varying Inefficiency
274(5)
11 Productivity and Profitability Decomposition
279(32)
11.1 Introduction
279(1)
11.2 Productivity, Technical Efficiency, and Profitability
280(5)
11.3 Productivity and Profitability Decomposition
285(26)
11.3.1 Total Factor Productivity Decomposition: The Production Function Approach
286(8)
11.3.2 Productivity Decomposition: The Cost Function Approach
294(6)
11.3.3 Multiple Outputs
300(11)
Part VI Looking Ahead
12 Looking Ahead
311(8)
12.1 Latent Class Models
311(1)
12.2 Zero-Inefficiency SF Models
312(1)
12.3 Selectivity in SF Models
313(1)
12.4 Modeling Good and Bad Outputs That Separate Technical Efficiency from Environmental Efficiency
313(1)
12.5 Two-Tier SF Models
314(1)
12.6 SF Models with Copula Functions (To Introduce Correlation between the Noise and Inefficiency Terms)
314(1)
12.7 Nonparametric and Semiparametric SF Models
314(1)
12.8 Testing Distribution Assumptions
315(4)
Appendix
A Deriving the Likelihood Functions of Single Equation Frontier Models
319(4)
B Deriving the Efficiency Estimates
323(3)
C Deriving Confidence Intervals
326(2)
D Bootstrapping Standard Errors of Marginal Effects on Inefficiency
328(3)
E Software and Estimation Commands
331(18)
E.1 Download and Install the User-Written Programs
331(1)
E.2 Download the Empirical Data and the Do-Files
331(1)
E.3 Cross-Sectional Models and Basic Utilities
331(7)
E.3.1 sfmodel
331(3)
E.3.2 sf_init
334(1)
E.3.3 sf_srch
335(1)
E.3.4 sf_transform
336(1)
E.3.5 sf_predict
336(2)
E.3.6 sf_mixtable
338(1)
E.4 System Models
338(4)
E.4.1 sfsystem
338(1)
E.4.2 showini
339(1)
E.4.3 sfsysem_profitshares
340(2)
E.5 Panel Data Models
342(3)
E.5.1 sfpan
342(2)
E.5.2 sf_fixeff
344(1)
E.6 Primal Models
345(4)
E.6.1 sfprim
345(2)
E.6.2 sf cst_compare
347(1)
E.6.3 sf pft_compare
348(1)
Bibliography 349(8)
Index 357
Subal C. Kumbhakar is a distinguished research professor at the State University of New York, Binghamton. He specializes in productivity and efficiency analysis, with particular emphasis on the theory and application of stochastic frontier (SF) models. He has developed numerous SF models for both cross-sectional and panel models in a single-equation set-up, as well as in a set-up with simultaneous equations. He is co-editor of Empirical Economics and guest editor of special issues of the Journal of Econometrics, Empirical Economics, the Journal of Productivity Analysis, and the Indian Economic Review. He is associate editor and editorial board member of Technological Forecasting and Social Change: An International Journal, the Journal of Productivity Analysis, the International Journal of Business and Economics, and Macroeconomics and Finance in Emerging Market Economies. He is also the co-author of Stochastic Frontier Analysis (Cambridge, 2000). Hung-Jen Wang is Professor of Economics at the National Taiwan University. His research interests include stochastic frontier analysis and empirical macroeconomics. He has published research papers in the Journal of Econometrics, the Journal of Business and Economic Statistics, Econometric Review, Economic Inquiry, the Journal of Productivity Analysis, and Economics Letters. He was a co-editor of Pacific Economic Review and is currently associate editor of Empirical Economics and the Journal of Productivity Analysis. Alan P. Horncastle has been a professional economist for more than twenty years and leads Oxera's work on performance assessment. He has provided efficiency advice for companies and regulatory authorities in the energy, transport, water, financial services, and communications sectors across Europe for business planning, transactions, regulatory reviews, Competition Commission cases, and court hearings. He has published papers in the Journal of the Operational Research Society, the Journal of Regulatory Economics, the Competition Law Journal, and Utilities Policy and has contributed chapters to Liberalization of the Postal and Delivery Sector and Emerging Issues in Competition, Collusion and Regulation of Network Industries.
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