Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques.
Author(s): David F. Hendry, Bent Nielsen
Series: 1
Edition: 1
Publisher: Princeton University Press
Year: 2007
Language: English
Pages: 365
Tags: Econometrics
Preface ix
Data and software xi
Chapter 1. The Bernoulli model 1
1.1 Sample and population distributions 1
1.2 Distribution functions and densities 4
1.3 The Bernoulli model 6
1.4 Summary and exercises 12
Chapter 2. Inference in the Bernoulli model 14
2.1 Expectation and variance 14
2.2 Asymptotic theory 19
2.3 Inference 23
2.4 Summary and exercises 26
Chapter 3. A first regression model 28
3.1 The US census data 28
3.2 Continuous distributions 29
3.3 Regression model with an intercept 32
3.4 Inference 38
3.5 Summary and exercises 42
Chapter 4. The logit model 47
4.1 Conditional distributions 47
4.2 The logit model 52
4.3 Inference 58
4.4 Mis-specification analysis 61
4.5 Summary and exercises 63
Chapter 5. The two-variable regression model 66
5.1 Econometric model 66
5.2 Estimation 69
5.3 Structural interpretation 76
5.4 Correlations 78
5.5 Inference 81
vi CONTENTS
5.6 Summary and exercises 85
Chapter 6. The matrix algebra of two-variable regression 88
6.1 Introductory example 88
6.2 Matrix algebra 90
6.3 Matrix algebra in regression analysis 94
6.4 Summary and exercises 96
Chapter 7. The multiple regression model 98
7.1 The three-variable regression model 98
7.2 Estimation 99
7.3 Partial correlations 104
7.4 Multiple correlations 107
7.5 Properties of estimators 109
7.6 Inference 110
7.7 Summary and exercises 118
Chapter 8. The matrix algebra of multiple regression 121
8.1 More on inversion of matrices 121
8.2 Matrix algebra of multiple regression analysis 122
8.3 Numerical computation of regression estimators 124
8.4 Summary and exercises 126
Chapter 9. Mis-specification analysis in cross sections 127
9.1 The cross-sectional regression model 127
9.2 Test for normality 128
9.3 Test for identical distribution 131
9.4 Test for functional form 134
9.5 Simultaneous application of mis-specification tests 135
9.6 Techniques for improving regression models 136
9.7 Summary and exercises 138
Chapter 10. Strong exogeneity 140
10.1 Strong exogeneity 140
10.2 The bivariate normal distribution 142
10.3 The bivariate normal model 145
10.4 Inference with exogenous variables 150
10.5 Summary and exercises 151
Chapter 11. Empirical models and modeling 154
11.1 Aspects of econometric modeling 154
11.2 Empirical models 157
11.3 Interpreting regression models 161
11.4 Congruence 166
11.5 Encompassing 169
11.6 Summary and exercises 173
