1 Introduction
1.1 Objectives of the course and the lecture notes
1.2 An extremely short introduction to R
1.3 R packages
1.4 Data sets
1.5 Mathematical and statistical methods
2 Primal Approach: Production Function
2.1 Theory
2.2 Productivity measures
2.3 Linear production function
2.4 Cobb-Douglas production function
2.5 Quadratic production function
2.6 Translog production function
2.7 Evaluation of different functional forms
2.8 Non-parametric production function
3 Dual Approach: Cost Functions
3.1 Theory
3.2 Cobb-Douglas cost function
3.3 Cobb-Douglas short-run cost function
3.4 Translog cost function
4 Dual Approach: Profit Function
4.1 Theory
4.2 Graphical illustration of profit and gross margin
4.3 Cobb-Douglas profit function
4.4 Cobb-Douglas short-run profit function
5 Efficiency Measures
5.1 Technical efficiency
5.2 Allocative efficiency, revenue efficiency, cost efficiency
5.3 Profit efficiency
5.4 Scale efficiency
6 Stochastic Frontier Analysis
6.1 Stochastic production frontiers
6.2 Stochastic cost frontiers
6.3 Analyzing the effects of z variables
6.4 Decomposition of cost efficiency
7 Data Envelopment Analysis (DEA)
7.1 Preparations
7.2 DEA with input-oriented efficiencies
7.3 DEA with output-oriented efficiencies
7.4 DEA with “super efficiencies”
7.5 DEA with graph hyperbolic efficiencies
8 Distance Functions
8.1 Theory
8.2 Cobb-Douglas output distance function
8.3 Translog output distance function
8.4 Cobb-Douglas input distance function
8.5 Translog input distance function
9 Panel Data and Technological Change
9.1 Average production functions with technological change
9.2 Frontier production functions with technological change
9.3 Analyzing productivity growths with Data Envelopment Analysis (DEA)