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E-raamat: Production and Efficiency Analysis with R

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 09-Jan-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319205021
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Production and Efficiency Analysis with RSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 09-Jan-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319205021
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This textbook introduces essential topics and techniques in production and efficiency analysis and shows how to apply these methods using the statistical software R. Numerous small simulations lead to a deeper understanding of random processes assumed in the models and of the behavior of estimation techniques. Step-by-step programming provides an understanding of advanced approaches such as stochastic frontier analysis and stochastic data envelopment analysis. The text is intended for master students interested in empirical production and efficiency analysis. Readers are assumed to have a general background in production economics and econometrics, typically taught in introductory microeconomics and econometrics courses.

Introduction.- Linear Production Model.- Production Functions.- Production Functions with Panel Data.- Introduction to Linear Programming.- Data Envelopment Analysis.- Stochastic Data Envelopment Analysis.- Stochastic Frontier Analysis.- Panel Data Stochastic Frontier Analysis.
1 Introduction
1(32)
1.1 A Short Introduction to R
1(9)
1.1.1 Objects
1(1)
1.1.2 Dataframes
2(1)
1.1.3 Sequences, Replications, and Loops
3(1)
1.1.4 Matrices
4(2)
1.1.5 Reading and Storing Data Files
6(1)
1.1.6 Probability Distributions
7(1)
1.1.7 Graphics
8(1)
1.1.8 Linear Regression
9(1)
1.2 Testing Linear Restrictions
10(5)
1.3 Maximum Likelihood Estimation
15(7)
1.3.1 The Basic Idea of Maximum Likelihood Estimation
15(1)
1.3.2 The Exponential Distribution
15(3)
1.3.3 Properties of ml-Estimators
18(4)
1.4 Numerical Optimization
22(8)
1.4.1 The General Form of Algorithms
22(1)
1.4.2 The Newton--Raphson Algorithm
23(1)
1.4.3 Two Simple Examples
24(6)
1.5 Recommended Readings
30(1)
1.6 Exercises
30(3)
References
32(1)
2 Linear Production Model
33(24)
2.1 The Theoretical Linear Production Model
33(8)
2.1.1 Industries and Goods
33(2)
2.1.2 Direct and Indirect Inputs
35(1)
2.1.3 The Employment Model
36(2)
2.1.4 A Numerical Example with R
38(3)
2.2 Input--Output Tables
41(3)
2.2.1 Aggregated Tables for Germany
41(1)
2.2.2 Some Identities
42(2)
2.3 Analyzing German Input--Output Tables 1991 and 2007
44(11)
2.3.1 Handling Input--Output Tables
44(2)
2.3.2 Some Empirical Results
46(9)
2.4 Recommended Readings
55(1)
2.5 Exercises
56(1)
References
56(1)
3 Production Functions
57(26)
3.1 Introduction
57(2)
3.2 The Cobb--Douglas Function
59(3)
3.2.1 Properties of the Function
59(1)
3.2.2 Estimating the CD with R
60(2)
3.3 The Constant Elasticity of Substitution Function
62(9)
3.3.1 Properties of the Function
63(3)
3.3.2 Estimating the CES with R
66(5)
3.4 The Transcendental Logarithmic Production Function
71(8)
3.4.1 Properties of the Function
72(3)
3.4.2 Estimating the TL with R
75(4)
3.5 Recommended Readings
79(1)
3.6 Exercises
80(3)
References
81(2)
4 Production Functions with Panel Data
83(30)
4.1 Introduction
83(1)
4.2 Introduction to Panel Data
84(11)
4.2.1 Unrestricted and Restricted Models
84(1)
4.2.2 Pooled, Within, Between
85(2)
4.2.3 Random Effects
87(7)
4.2.4 Fixed or Random?
94(1)
4.3 Panel Data Analysis with R
95(13)
4.3.1 Generating a Panel Data Set
95(3)
4.3.2 Some Transformations with Panel Data
98(1)
4.3.3 Estimating Panel Data Models Using the plm Package
99(5)
4.3.4 Dynamic Panel Data Models
104(4)
4.4 Recommended Readings
108(1)
4.5 Exercises
109(4)
References
111(2)
5 Linear Programming
113(22)
5.1 Introduction
113(7)
5.1.1 Cost Minimization
113(3)
5.1.2 Profit Maximization
116(1)
5.1.3 General Notation
117(1)
5.1.4 Convex Sets
118(1)
5.1.5 Slacks and Surpluses
119(1)
5.2 Simplex Algorithm
120(9)
5.2.1 Maximization
120(2)
5.2.2 Minimization
122(3)
5.2.3 Duality
125(2)
5.2.4 Simplex Tableau
127(2)
5.3 Solving the Simplex Using R
129(3)
5.3.1 The Setup of Simplex Tableaus
129(1)
5.3.2 Solving Some Examples
129(3)
5.4 Recommended Readings
132(1)
5.5 Exercises
133(2)
References
134(1)
6 Data Envelopment Analysis
135(26)
6.1 Introduction
135(6)
6.1.1 Productivity and Efficiency
135(1)
6.1.2 One Input--One Output
136(1)
6.1.3 Two Inputs--One Output
137(1)
6.1.4 One Input--Two Outputs
138(2)
6.1.5 Fixed and Variable Weights
140(1)
6.2 Charnes--Cooper--Rhodes-Model
141(10)
6.2.1 Introduction
141(1)
6.2.2 Programming Problem
142(1)
6.2.3 Examples
143(1)
6.2.4 Production Correspondence
144(4)
6.2.5 Output-Oriented Model
148(1)
6.2.6 Returns to Scale in DEA Models
149(2)
6.3 Data Envelopment Analysis with R
151(6)
6.3.1 Benchmarking Package
151(1)
6.3.2 Examples
151(6)
6.4 Recommended Reading
157(1)
6.5 Exercises
158(3)
References
159(2)
7 Stochastic Data Envelopment Analysis
161(22)
7.1 Introduction
161(1)
7.2 Population and Simple Random Samples
162(2)
7.3 The Bootstrap
164(3)
7.4 Sampling and DEA
167(4)
7.5 DEA and the Naive Bootstrap
171(5)
7.5.1 Some Notation
171(1)
7.5.2 Bootstrap Estimation Based on a Specific Sample
172(3)
7.5.3 Bootstrap Estimation Based on All Samples
175(1)
7.6 The Simar--Wilson Approach
176(5)
7.6.1 Strongly Simplified Algorithm
176(2)
7.6.2 The Simar--Wilson Algorithm
178(3)
7.7 Recommended Reading
181(1)
7.8 Exercises
181(2)
References
182(1)
8 Stochastic Frontier Analysis
183(20)
8.1 Introduction
183(1)
8.2 Production and Inefficiency
183(8)
8.2.1 Observed and Efficient Production
184(1)
8.2.2 Production Frontier and Deviations
184(1)
8.2.3 Data Generation
185(4)
8.2.4 Corrected Ordinary Least Squares
189(1)
8.2.5 Corrected Ordinary Least Squares Using R
189(2)
8.3 Maximum Likelihood Estimation
191(8)
8.3.1 The Log-Likelihood
192(1)
8.3.2 Estimation of Individual Inefficiency Terms
193(2)
8.3.3 Maximum Likelihood with R
195(4)
8.4 Recommended Reading
199(1)
8.5 Exercises
200(3)
References
201(2)
9 Panel Data Stochastic Frontier Analysis
203(20)
9.1 Introduction
203(1)
9.2 Homogeneity and Firm Specific Time Invariant Inefficiency
204(4)
9.3 Time Varying Firm Specific Inefficiency
208(4)
9.4 The Stochastic Frontier Model with Time Varying Inefficiency
212(9)
9.4.1 Sketching the Idea of the Approach
213(1)
9.4.2 Obtaining the Log-Likelihood
214(3)
9.4.3 Generating Data According to the Model
217(4)
9.5 Recommended Reading
221(1)
9.6 Exercise
221(2)
References
222(1)
Functions Index 223(2)
Subject Index 225
Prof. Dr. Andreas Behr has held a chair for Statistics at the University Duisburg-Essen since 2009. He studied economics and business administration in Frankfurt/Main, where he received his doctoral degree for a thesis on intra-industry trade. After spending three months as visiting researcher in Fukuoka, Japan, he completed his postdoctoral thesis on investment and liquidity constraints in Frankfurt. From 2003-2008 he taught at Muenster University. His research interests include efficiency analysis, economics statistics, panel data analysis and survey methods.
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