Christiaan Heij is Associate Professor at the Econometric Institute of the Erasmus University in Rotterdam and specialises in econometrics and statistics.

Paul de Boer is Assistant Professor at the Econometric Institute of the Erasmus University in Rotterdam and specialises in econometrics and statistics.

Philip Hans Franses is Professor of Applied Econometrics and Professor of Marketing Research, both at the Erasmus University Rotterdam. He has published in leading international journals on applied econometrics, time series analysis, empirical finance, and marketing research. He is the (co-)author of various books published by Oxford University Press and Cambridge University Press.

Teun Kloek is Professor Emeritus of Econometrics at Erasmus University Rotterdam. He has published in leading international journals on econometric theory, applied econometrics and quantitative economics.

Herman K. van Dijk is Professor of Econometrics and director of the Econometric Institute of the Erasmus University in Rotterdam. His fields of research are Bayesian Inference and Decision Analysis in Econometrics, Computational Economics, Stochastic Trends and Cycles in Time Series Econometrics and Income Distributions.

• Introduction


• 1 Review of Statistics


• 1.1: Descriptive statistics


• 1.2: Random variables


• 1.3: Parameter estimation


• 1.4: Tests of hypotheses


• Summary, further reading, and keywords


• Exercises


• 2 Simple Regression


• 2.1: Least squares


• 2.2: Accuracy of least squares


• 2.3: Significance tests


• 2.4: Prediction


• Summary, further reading, and keywords


• Exercises


• 3 Multiple Regression


• 3.1: Least squares in matrix form


• 3.2: Adding or deleting variables


• 3.3: The accuracy of estimates


• 3.4: The F-test


• Summary, further reading, and keywords


• Exercises


• 4 Non-Linear Methods


• 4.1: Asymptotic analysis


• 4.2: Non-linear regression


• 4.3: Maximum likelihood


• 4.4: Generalized method of moments


• Summary, further reading, and keywords


• Exercises


• 5 Diagnostic Tests and Model Adjustments


• 5.1: Introduction


• 5.2: Functional form and explanatory variables


• 5.3: Varying parameters


• 5.4: Heteroskedasticity


• 5.5: Serial correlation


• 5.6: Disturbance distribution


• 5.7: Endogenous regressors and instrumental variables


• 5.8: Illustration: Salaries of top managers


• Summary, further reading, and keywords


• Exercises


• 6 Qualitative and Limited Dependent Variables


• 6.1: Binary response


• 6.2: Multinomial data


• 6.3: Limited dependent variables


• Summary, further reading, and keywords


• Exercises


• 7 Time Series and Dynamic Models


• 7.1: Models for stationary time series


• 7.2: Model estimation and selection


• 7.3: Trends and seasonals


• 7.4: Non-linearities and time-varying volatility


• 7.5: Regression models with lags


• 7.6: Vector autoregressive models


• 7.7: Other multiple equation models


• Summary, further reading, and keywords


• Exercises


• Appendix A: Matrix Methods


• A.1: Summations


• A.2: Vectors and matrices


• A.3: Matrix addition and multiplication


• A.4: Transpose, trace, and inverse


• A.5: Determinant, rank, and eigenvalues


• A.6: Positive (semi)definite matrices and projections


• A.7: Optimization of a function of several variables


• A.8: Concentration and the Lagrange method


• Exercise


• Appendix B: Data Sets


• Index