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.
''. . . students will find
the contents of this book to be a very helpful guide . . . Because of its wide coverage
and careful presentation the book should be useful for a diverse group of students in many
countries and interested in a variety of areas of applications.''
C. W. J. Granger, Nobel Laureate
''Most econometric texts can
be described as either primarily theoretical or primarily applied. This is the first text
I've seen that does a really nice job of bridging the gap between the two in a single
unified whole. . . . I can strongly recommend this book to anyone desiring a firm
understanding of both where econometric methods come from and how they are used in
practice.''
James D. Hamilton, University of California, San Diego
''. . . superbly presented,
the coverage is thorough, the technical rigour is sensibly balanced, and the empirical
examples demonstrate the techniques effectively. The exercises are stimulating, the
answers are insightful, and the exposition in the background material is excellent. It
will appeal very strongly to researchers, instructors and students.''
Michael McAleer, University of Western Australia
''. . . a thorough
introduction to the basic principles of econometrics . . . The strong link between theory
and applications provides great motivation for studying econometrics.''
Helmut Lütkepohl, European University Institute, Florence
''. . . meticulously crafted
to give an almost seamless transition between learning and doing econometrics . . . There
is something here for all students of econometrics.''
Michael P. Clements, Warwick University
This rigorous textbook
provides students with a working understanding and hands-on experience of current
econometrics. It covers basic econometric methods and addresses the creative process of
model building. Using real-world examples and exercises, it focuses on regression and
covers choice data and time series data. Perfect for advanced undergraduate students, new
graduate students, and applied researchers.
Christiaan Heij,
Associate Professor at the Econometric Institute, Paul de Boer, Assistant Professor
at the Econometric Institute, Philip Hans Franses, Professor of Applied
Econometrics and Professor of Marketing Research, Teun Kloek, Professor Emeritus of
Econometrics, and Herman K. van Dijk, Professor of Econometrics.
Table of Contents
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
787 pages