Yves Croissant, Giovanni Millo

Panel Data Econometrics with R (eBook, PDF)

• Format: PDF

Produktbeschreibung

Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book's accompanying website.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 328
• Erscheinungstermin: 13. August 2018
• Englisch
• ISBN-13: 9781118949177
• Artikelnr.: 54222051

Autorenporträt

Yves Croissant, Professor of Economics, CEMOI, Faculté de Droit et d'Economie, Université de La Réunion, France Giovanni Millo, Senior Economist, Group Insurance Research, Assicurazioni Generali S.p.A., Trieste, Italy

Inhaltsangabe

Preface xiii

Acknowledgments xvii

About the CompanionWebsite xix

1 Introduction 1

1.1 Panel Data Econometrics: A Gentle Introduction 1

1.1.1 Eliminating Unobserved Components 2

1.1.1.1 Differencing Methods 2

1.1.1.2 LSDV Methods 2

1.1.1.3 Fixed Effects Methods 2

1.2 R for Econometric Computing 6

1.2.1 The Modus Operandi of R 7

1.2.2 Data Management 8

1.2.2.1 Outsourcing to Other Software 8

1.2.2.2 Data ManagementThrough Formulae 8

1.3 plm for the Casual R User 8

1.3.1 R for the Matrix Language User 9

1.3.2 R for the User of Econometric Packages 10

1.4 plm for the Proficient R User 11

1.4.1 Reproducible EconometricWork 12

1.4.2 Object-orientation for the User 13

1.5 plm for the R Developer 13

1.5.1 Object-orientation for Development 14

1.6 Notations 17

1.6.1 General Notation 18

1.6.2 Maximum Likelihood Notations 18

1.6.3 Index 18

1.6.4 The Two-way Error Component Model 18

1.6.5 Transformation for the One-way Error Component Model 19

1.6.6 Transformation for the Two-ways Error Component Model 20

1.6.7 Groups and Nested Models 20

1.6.8 Instrumental Variables 20

1.6.9 Systems of Equations 20

1.6.10 Time Series 21

1.6.11 Limited Dependent and Count Variables 21

1.6.12 Spatial Panels 21

2 The Error Component Model 23

2.1 Notations and Hypotheses 23

2.1.1 Notations 23

2.1.2 Some Useful Transformations 24

2.1.3 Hypotheses Concerning the Errors 25

2.2 Ordinary Least Squares Estimators 27

2.2.1 Ordinary Least Squares on the Raw Data: The Pooling Model 27

2.2.2 The between Estimator 28

2.2.3 The within Estimator 29

2.3 The Generalized Least Squares Estimator 33

2.3.1 Presentation of the GLS Estimator 34

2.3.2 Estimation of the Variances of the Components of the Error 35

2.4 Comparison of the Estimators 39

2.4.1 Relations between the Estimators 39

2.4.2 Comparison of the Variances 40

2.4.3 Fixed vs Random Effects 40

2.4.4 Some Simple Linear Model Examples 42

2.5 The Two-ways Error Components Model 47

2.5.1 Error Components in the Two-ways Model 47

2.5.2 Fixed and Random Effects Models 48

2.6 Estimation of a Wage Equation 49

3 Advanced Error Components Models 53

3.1 Unbalanced Panels 53

3.1.1 Individual Effects Model 53

3.1.2 Two-ways Error Component Model 54

3.1.2.1 Fixed Effects Model 55

3.1.2.2 Random Effects Model 56

3.1.3 Estimation of the Components of the Error Variance 57

3.2 Seemingly Unrelated Regression 64

3.2.1 Introduction 64

3.2.2 Constrained Least Squares 65

3.2.3 Inter-equations Correlation 66

3.2.4 SUR With Panel Data 67

3.3 The Maximum Likelihood Estimator 71

3.3.1 Derivation of the Likelihood Function 71

3.3.2 Computation of the Estimator 73

3.4 The Nested Error Components Model 74

3.4.1 Presentation of the Model 74

3.4.2 Estimation of the Variance of the Error Components 75

4 Tests on Error Component Models 83

4.1 Tests on Individual and/or Time Effects 83

4.1.1 F Tests 84

4.1.2 Breusch-Pagan Tests 84

4.2 Tests for Correlated Effects 88

4.2.1 The Mundlak Approach 89

4.2.2 Hausman Test 90