This revised and updated edition of A Guide to Modern Econometricscontinues to explore a wide range of topics in modern econometrics by focusing on what is important for doing and understanding empirical work. It serves as a guide to alternative techniques with the emphasis on the intuition behind the approaches and their practical relevance.

New material includes Monte Carlo studies, weak instruments, nonstationary panels, count data, duration models and the estimation of treatment effects.

Features of this book include:

• Coverage of a wide range of topics, including time series analysis, cointegration, limited dependent variables, panel data analysis and the generalized method of moments
• Empirical examples drawn from a wide variety of fields including labour economics, finance, international economics, environmental economics and macroeconomics
• End-of-chapter exercises review key concepts in light of empirical examples
• A supplementary website, featuring additional materials including data sets for illustrations and exercises, can be found at www.wileyeurope.com/go/verbeek2ed

Verbeek (finance, Erasmus University) brings an empirical perspective to the discussion of a range of modern econometric topics, including time series analysis, cointegration, limited dependent variables, panel data analysis, and the generalized method of moments. Using examples from labor economics, finance, international economics, environmental economics, and macroeconomics, he explains the rationale behind various techniques and details their practical application.
Annotation ©2004 Book News, Inc., Portland, OR (booknews.com)

A Guide to Modern Econometrics has become established as a highly successful textbook. It serves as a guide to alternative techniques in econometrics with an emphasis on the practical application of these approaches.

A guide to Modern Econometrics, third edition features:

• Up-to-date references in line with current literature on the subjects.
• New material on panel data and pseudo panels.
• Improved exposition of tough subjects, like multicollinearity and instrumental variables.
• Increased focus on robust inference.
• New empirical illustrations including panel data, discrete choice models and the linear model as a forecasting tool.
• New supplementary material including PowerPoint slides for lecturers, data sets and solutions to selected exercises in each chapter, can be found at www.wileyeurope.com/college/verbeek

This highly successful text focuses on exploring alternative techniques, combined with a practical emphasis, A guide to alternative techniques with the emphasis on the intuition behind the approaches and their practical reference, this new edition builds on the strengths of the second edition and brings the text completely up-to-date.

Marno Verbeekis 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 Université Panthéon-Assas Paris II. He has published in a wide variety of international journals.

'Preface.

1 Introduction.

1.1 About Econometrics.

1.2 The Structure of this Book.

1.3 Illustrations and Exercises. 

2 An Introduction to Linear Regression.

2.1 Ordinary Least Squares as an Algebraic Tool.

2.2 The Linear Regression Model.

2.3 Small Sample Properties of the OLS Estimator.

2.4 Goodness-of-fit.

2.5 Hypothesis Testing.

2.6 Asymptotic Properties of the OLS Estimator.

2.7 Illustration: The Capital Asset Pricing Model.

2.8 Multicollinearity.

2.9 Prediction.

3 Interpreting and Comparing Regression Models.

3.1 Interpreting the Linear Model.

3.2 Selecting the Set of Regressors.

3.3 Misspecifying the Functional Form.

3.4 Illustration: Explaining House Prices.

3.5 Illustration: Predicting Stock Index Returns.

3.6 Illustration: Explaining Individual Wages.

4 Heteroskedasticity and Autocorrelation.

4.1 Consequences for the OLS Estimator.

4.2 Deriving an Alternative Estimator.

4.3 Heteroskedasticity.

4.4 Testing for Heteroskedasticity.

4.5 Illustration: Explaining Labour Demand.

4.6 Autocorrelation.

4.7 Testing for First-order Autocorrelation.

4.8 Illustration: The Demand for Ice Cream.

4.9 Alternative Autocorrelation Patterns.

4.10 What to do When you Find Autocorrelation?

4.11 Illustration: Risk Premia in Foreign Exchange Markets.

5 Endogeneity, Instrumental Variables and GMM.

5.1 A Review of the Properties of the OLS Estimator.

5.2 Cases Where the OLS Estimator Cannot be Saved.

5.3 The Instrumental Variables Estimator.

5.4 Illustration: Estimating the Returns to Schooling.

5.5 The Generalized Instrumental Variables Estimator.

5.6 The Generalized Method of Moments.

5.7 Illustration: Estimating Intertemporal Asset Pricing Models.

5.8 Concluding Remarks.

6 Maximum Likelihood Estimation and Specification Tests.

6.1 An Introduction to Maximum Likelihood.

6.2 Specification Tests.

6.3 Tests in the Normal Linear Regression Model.

6.4 Quasi-maximum Likelihood and Moment Conditions Tests.

7 Models with Limited Dependent Variables.

7.1 Binary Choice Models.

7.2 Multiresponse Models.

7.3 Models for Count Data.

7.4 Tobit Models.

7.5 Extensions of Tobit Models.

7.6 Sample Selection Bias.

7.7 Estimating Treatment Effects.

7.8 Duration Models.

8 Univariate Time Series Models.

8.1 Introduction.

8.2 General ARMA Processes.

8.3 Stationarity and Unit Roots.

8.4 Testing for Unit Roots.

8.5 Illustration: Long-run Purchasing Power Parity (Part 1).

8.6 Estimation of ARMA Models.

8.7 Choosing a Model.

8.8 Predicting with ARMA Models.

8.9 Illustration: The Expectations Theory of the Term Structure.

8.10 Autoregressive Conditional Heteroskedasticity.

8.11 What about Multivariate Models?

9 Multivariate Time Series Models.

Multivariate Time Series Models.

9.1 Dynamic Models with Stationary Variables.

9.2 Models with Nonstationary Variables.

9.3 Illustration: Long-run Purchasing Power Parity (Part 2).

9.4 Vector Autoregressive Models.

9.5 Cointegration: the Multivariate Case.

9.6 Illustration: Money Demand and Inflation.

9.7 Concluding Remark.

10 Models Based on Panel Data.

10.1 Introduction to Panel Data Modeling.

10.2 The Static Linear Model.

10.3 Illustration: Explaining Individual Wages.

10.4 Dynamic Linear Models.

10.5 Illustration: Explaining Capital Structure.

10.6 Nonstationarity, Unit Roots and Cointegration.

10.7 Models with Limited Dependent Variables.

10.8 Incomplete Panels and Selection Bias.

10.9 Pseudo Panels and Repeated Cross-sections.

A Vectors and Matrices.

A.1 Terminology.

A.2 Matrix Manipulations.

A.3 Properties of Matrices and Vectors.

A.4 Inverse Matrices.

A.5 Idempotent Matrices.

A.6 Eigenvalues and Eigenvectors.

A.7 Differentiation.

A.8 Some Least Squares Manipulations.

B Statistical and Distribution Theory.

B.1 Discrete Random Variables.

B.2 Continuous Random Variables.

B.3 Expectations and Moments.

B.4 Multivariate Distributions.

B.5 Conditional Distributions.

B.6 The Normal Distribution.

B.7 Related Distributions 

Bibliography.

Index.

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