Introduction to Econometric Production Analysis with R
Introduction to Econometric Production Analysis with R
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Introduction to Econometric Production Analysis with R

Last updated on 2019-05-27

About the Book

Knowledge about production technologies and producer behavior is important for politicians, business organizations, government administrations, financial institutions, and other national and international organizations that desire to know how contemplated policies and market conditions can affect production, prices, income, and resource utilization in agriculture as well as in other industries. The same knowledge is relevant for comparing firms with other firms and the “best practice”, e.g., for consulting farms or other firms or for regulating sectors with natural monopolies (e.g., electricity distribution, water supply, wastewater treatment). The participants of this course will obtain relevant competences in microeconomic production theory and applied econometric production analysis so that they can contribute to the knowledge about production technologies and producer behaviour. After having completed my courses in the field of econometric production analysis, the participants should be able to:

  • use econometric production analysis and efficiency analysis to analyze various real-world questions,
  • interpret the results of econometric production analyses and efficiency analyses,
  • choose a relevant approach for econometric production and efficiency analysis, and
  • critically evaluate the appropriateness of a specific econometric production analysis or efficiency analysis for analyzing a specific real-world question.

These lecture notes focus on practical applications of econometrics and microeconomic production theory. Hence, they complement textbooks in microeconomic production theory (rather than substituting them).

About the Author

Arne Henningsen
Arne Henningsen

Arne Henningsen is Associate Professor at the Department of Food and Resource Economics at the University of Copenhagen. He is specialized in econometric production analysis, e.g. using stochastic frontier analysis, nonparametric kernel regression, and other microeconometric methods. The author has published (with co-authors) several R packages (e.g. "frontier" for stochastic frontier analysis, "micEcon" for microeconomic analysis, "micEconCES" for estimating the Constant Elasticity of Substitution (CES) function, "micEconSNQP" for estimating the normalized quadratic profit function; "micEconAids" for estimating the Almost Ideal Demand System (AIDS), "systemfit" for estimating systems of equations, "sampleSelection" for estimating Heckman-type sample selection models, "censReg" for censored regression, and "maxLik" for general maximum likelihood (ML) estimation.

Table of Contents

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) 

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