A guide to modern econometrics

This highly successful text serves as a guide to alternative techniques in econometrics with an emphasis on the practical application of these approaches.

The 4th Edition features:

Coverage of a wide range of topics, including time series analysis, cointegration, limited dependent variables, panel data analysis and the generalized method of moments. Intuitive presentation and discussion, with a focus on implementation and practical relevance. A large number of empirical illustrations taken from a wide variety of fields, including international economics, finance, labour economics and macroeconomics. Increased focus on robust inference and small sample properties. End-of-chapter exercises, both theoretical and empirical, reviewing key concepts. Updated and expanded coverage, on various topics such as missing data, outliers, forecast evaluation, the estimation of treatment effects and panel unit root tests. Supplementary material, including PowerPoint slides for lecturers, data sets of the empirical illustrations and exercises, and solutions to selected exercises in each chapter, available at
www.wileyeurope.com/college/verbeek

Author Notes

Marno Verbeek is Professor of Finance at the Rotterdam School of Management and the Econometric Institute of Erasmus University, Rotterdam. He held previous positions at KU Leuven and Tilburg University, and visiting appointments at Trinity College Dublin and Universit#65533; Panth#65533;on-Assas Paris II. He has published in a wide variety of international journals.

PREFACE	p. xiii
1 Introduction	p. 1
1.1 About Econometrics	p. 1
1.2 The Structure of this Book	p. 3
1.3 Illustrations and Exercises	p. 4
2 An Introduction to Linear Regression	p. 6
2.1 Ordinary Least Squares as an Algebraic Tool	p. 7
2.2 The Linear Regression Model	p. 12
2.3 Small Sample Properties of the OLS Estimator	p. 15
2.4 Goodness-of-fit	p. 20
2.5 Hypothesis Testing	p. 22
2.6 Asymptotic Properties of the OLS Estimator	p. 32
2.7 Illustration: The Capital Asset Pricing Model	p. 38
2.8 Multicollinearity	p. 43
2.9 Missing Data, Outliers and Influential Observations	p. 47
2.10 Prediction	p. 52
3 Interpreting and Comparing Regression Models	p. 58
3.1 Interpreting the Linear Model	p. 58
3.2 Selecting the Set of Regressors	p. 62
3.3 Misspecifying the Functional Form	p. 70
3.4 Illustration: Explaining House Prices	p. 72
3.5 Illustration: Predicting Stock Index Returns	p. 76
3.6 Illustration: Explaining Individual Wages	p. 81
4 Heteroskedasticity and Autocorrelation	p. 94
4.1 Consequences for the OLS Estimator	p. 94
4.2 Deriving an Alternative Estimator	p. 96
4.3 Heteroskedasticity	p. 97
4.4 Testing for Heteroskedasticity	p. 105
4.5 Illustration: Explaining Labour Demand	p. 107
4.6 Autocorrelation	p. 112
4.7 Testing for First-order Autocorrelation	p. 116
4.8 Illustration: The Demand for Ice Cream	p. 119
4.9 Alternative Autocorrelation Patterns	p. 122
4.10 What to do When you Find Autocorrelation?	p. 123
4.11 Illustration: Risk Premia in Foreign Exchange Markets	p. 127
5 Endogenous Regressors, Instrumental Variables and GMM	p. 137
5.1 A Review of the Properties of the OLS Estimator	p. 138
5.2 Cases Where the OLS Estimator Cannot be Saved	p. 141
5.3 The Instrumental Variables Estimator	p. 148
5.4 Illustration: Estimating the Returns to Schooling	p. 154
5.5 The Generalized Instrumental Variables Estimator	p. 158
5.6 The Generalized Method of Moments	p. 166
5.7 Illustration: Estimating Intertemporal Asset Pricing Models	p. 171
6 Maximum Likelihood Estimation and Specification Tests	p. 179
6.1 An Introduction to Maximum Likelihood	p. 180
6.2 Specification Tests	p. 189
6.3 Tests in the Normal Linear Regression Model	p. 195
6.4 Quasi-maximum Likelihood and Moment Conditions Tests	p. 199
7 Models with Limited Dependent Variables	p. 206
7.1 Binary Choice Models	p. 207
7.3 Models for Count Data	p. 231
7.4 Tobit Models	p. 238
7.5 Extensions of Tobit Models	p. 247
7.6 Sample Selection Bias	p. 257
7.7 Estimating Treatment Effects	p. 260
7.8 Duration Models	p. 268
8 Univariate Time Series Models	p. 278
8.1 Introduction	p. 279
8.2 General ARMA Processes	p. 284
8.3 Stationarity and Unit Roots	p. 289
8.4 Testing for Unit Roots	p. 291
8.5 Illustration: Long-run Purchasing Power Parity (Part 1)	p. 300
8.6 Estimation of ARMA Models	p. 304
8.7 Choosing a Model	p. 306
8.8 Illustration: The Persistence of Inflation	p. 311
8.9 Predicting with ARMA Models	p. 314
8.10 Illustration: The Expectations Theory of the Term Structure	p. 320
8.11 Autoregressive Conditional Heteroskedasticity	p. 325
8.12 What about Multivariate Models?	p. 333
9 Multivariate Time Series Models	p. 338
9.1 Dynamic Models with Stationary Variables	p. 339
9.2 Models with Nonstationary Variables	p. 342
9.3 Illustration: Long-run Purchasing Power Parity (Part 2)	p. 348
9.4 Vector Autoregressive Models	p. 350
9.5 Cointegration: the Multivariate Case	p. 354
9.6 Illustration: Money Demand and Inflation	p. 362
10 Models Based on Panel Data	p. 372
10.1 Introduction to Panel Data Modelling	p. 373
10.2 The Static Linear Model	p. 376
10.3 Illustration: Explaining Individual Wages	p. 394
10.4 Dynamic Linear Models	p. 396
10.5 Illustration: Explaining Capital Structure	p. 405
10.6 Panel Time Series	p. 410
10.7 Models with Limited Dependent Variables	p. 417
10.8 Incomplete Panels and Selection Bias	p. 425
10.9 Pseudo Panels and Repeated Cross-Sections	p. 430
A Vectors and Matrices	p. 441
A.1 Terminology	p. 441
A.2 Matrix Manipulations	p. 442
A.3 Properties of Matrices and Vectors	p. 443
A.4 Inverse Matrices	p. 444
A.5 Idempotent Matrices	p. 445
A.6 Eigenvalues and Eigenvectors	p. 445
A.7 Differentiation	p. 446
A.8 Some Least Squares Manipulations	p. 447
B Statistical and Distribution Theory	p. 449
B.1 Discrete Random Variables	p. 449
B.2 Continuous Random Variables	p. 450
B.3 Expectations and Moments	p. 451
B.4 Multivariate Distributions	p. 452
B.5 Conditional Distributions	p. 453
B.6 The Normal Distribution	p. 454
B.7 Related Distributions	p. 457
Bibliography	p. 459
Index	p. 477

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