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Produktbild: An Introduction to Econometric Theory

An Introduction to Econometric Theory

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

05.10.2018

Verlag

Wiley

Seitenzahl

256

Maße (L/B/H)

25,1/17,2/2 cm

Gewicht

529 g

Sprache

Englisch

ISBN

978-1-119-48488-2

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

05.10.2018

Verlag

Wiley

Seitenzahl

256

Maße (L/B/H)

25,1/17,2/2 cm

Gewicht

529 g

Sprache

Englisch

ISBN

978-1-119-48488-2

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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Die Leseprobe wird geladen.
  • Produktbild: An Introduction to Econometric Theory

  • List of Figures ix

    Preface xi

    About the CompanionWebsite xv

    Part I Fitting 1

    1 Elementary Data Analysis 3

    1.1 Variables and Observations 3

    1.2 Summary Statistics 4

    1.3 Correlation 6

    1.4 Regression 10

    1.5 Computing the Regression Line 12

    1.6 Multiple Regression 16

    1.7 Exercises 18

    2 Matrix Representation 21

    2.1 Systems of Equations 21

    2.2 Matrix Algebra Basics 23

    2.3 Rules of Matrix Algebra 26

    2.4 Partitioned Matrices 27

    2.5 Exercises 28

    3 Solving the Matrix Equation 31

    3.1 Matrix Inversion 31

    3.2 Determinant and Adjoint 34

    3.3 Transposes and Products 37

    3.4 Cramer's Rule 38

    3.5 Partitioning and Inversion 39

    3.6 A Note on Computation 41

    3.7 Exercises 43

    4 The Least Squares Solution 47

    4.1 Linear Dependence and Rank 47

    4.2 The General Linear Regression 50

    4.3 Definite Matrices 52

    4.4 Matrix Calculus 56

    4.5 Goodness of Fit 57

    4.6 Exercises 59

    Part II Modelling 63

    5 Probability Distributions 65

    5.1 A Random Experiment 65

    5.2 Properties of the Normal Distribution 68

    5.3 Expected Values 72

    5.4 Discrete Random Variables 75

    5.5 Exercises 80

    6 More on Distributions 83

    6.1 Random Vectors 83

    6.2 The Multivariate Normal Distribution 84

    6.3 Other Continuous Distributions 87

    6.4 Moments 90

    6.5 Conditional Distributions 92

    6.6 Exercises 94

    7 The Classical RegressionModel 97

    7.1 The Classical Assumptions 97

    7.2 The Model 99

    7.3 Properties of Least Squares 101

    7.4 The Projection Matrices 103

    7.5 The Trace 104

    7.6 Exercises 106

    8 The Gauss-Markov Theorem 109

    8.1 A Simple Example 109

    8.2 Efficiency in the General Model 111

    8.3 Failure of the Assumptions 113

    8.4 Generalized Least Squares 114

    8.5 Weighted Least Squares 116

    8.6 Exercises 118

    Part III Testing 121

    9 Eigenvalues and Eigenvectors 123

    9.1 The Characteristic Equation 123

    9.2 Complex Roots 124

    9.3 Eigenvectors 126

    9.4 Diagonalization 128

    9.5 Other Properties 130

    9.6 An Interesting Result 131

    9.7 Exercises 133

    10 The Gaussian RegressionModel 135

    10.1 Testing Hypotheses 135

    10.2 Idempotent Quadratic Forms 137

    10.3 Confidence Regions 140

    10.4 t Statistics 141

    10.5 Tests of Linear Restrictions 144

    10.6 Constrained Least Squares 146

    10.7 Exercises 149

    11 Partitioning and Specification 153

    11.1 The Partitioned Regression 153

    11.2 Frisch-Waugh-Lovell Theorem 155

    11.3 Misspecification Analysis 156

    11.4 Specification Testing 159

    11.5 Stability Analysis 160

    11.6 Prediction Tests 162

    11.7 Exercises 163

    Part IV Extensions 167

    12 Random Regressors 169

    12.1 Conditional Probability 169

    12.2 Conditional Expectations 170

    12.3 StatisticalModels Contrasted 174

    12.4 The Statistical Assumptions 176

    12.5 Properties of OLS 178

    12.6 The Gaussian Model 182

    12.7 Exercises 183

    13 Introduction to Asymptotics 187

    13.1 The Lawof Large Numbers 187

    13.2 Consistent Estimation 192

    13.3 The Central LimitTheorem 195

    13.4 Asymptotic Normality 198

    13.5 Multiple Regression 201

    13.6 Exercises 203

    14 Asymptotic Estimation Theory 207

    14.1 Large Sample Efficiency 207

    14.2 Instrumental Variables 208

    14.3 Maximum Likelihood 210

    14.4 Gaussian ML 213

    14.5 Properties of ML Estimators 214

    14.6 Likelihood Inference 216

    14.7 Exercises 218

    Part V Appendices 221

    A The Binomial Coefficients 223

    B The Exponential Function 225

    C Essential Calculus 227

    D The Generalized Inverse 229

    Recommended Reading 233

    Index 235