Econometric Methods with Applications in Business and Economics
Christiaan Heij, Paul de Boer, Philip Hans Franses, Teun Kloek, Herman van Dijk
ISBN 0-19-926801-0
Reviews
''. . . 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
Description
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.
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.
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
