Christiaan Heij, Paul de Boer, Philip Hans Franses, Teun Kloek and Herman K. van Dijk
All at the Erasmus University in Rotterdam
Nowadays applied work in business and economics requires a solid understanding of econometric methods to support decision-making. Combining a solid exposition of econometric methods with an application-oriented approach, this rigorous textbook provides students with a working understanding and
hands-on experience of current econometrics.
Taking a 'learning by doing' approach, it covers basic econometric methods (statistics, simple and multiple regression, nonlinear regression, maximum likelihood, and generalized method of moments), and addresses the creative process of model
building with due attention to diagnostic testing and model improvement. Its last part is devoted to two major application areas: the econometrics of choice data (logit and probit, multinomial and ordered choice, truncated and censored data, and duration data) and the econometrics of time series
data (univariate time series, trends, volatility, vector autoregressions, and a brief discussion of SUR models, panel data, and simultaneous equations).
· Real-world text examples and practical exercise questions stimulate active learning and show how econometrics can solve practical
questions in modern business and economic management.
· Focuses on the core of econometrics, regression, and covers two major advanced topics, choice data with applications in marketing and micro-economics, and time series data with applications in finance and macro-economics.
· Learning-support features include concise, manageable sections of text, frequent cross-references to related and background material, summaries, computational schemes, keyword lists, suggested further reading, exercise sets, and online data sets and solutions.
· Derivations and theory
exercises are clearly marked for students in advanced courses.
This textbook is perfect for advanced undergraduate students, new graduate students, and applied researchers in econometrics, business, and economics, and for researchers in other fields that draw on modern applied
econometrics.
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
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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.