Providing an understanding and experience of econometrics, this book covers basic econometric methods and addresses the creative process of model building. Using examples and exercises, it focuses on regression and covers choice data and time series data. It is aimed at undergraduate students, new graduate students, and applied researchers.
Providing an understanding and experience of econometrics, this book covers basic econometric methods and addresses the creative process of model building. Using examples and exercises, it focuses on regression and covers choice data and time series data. It is aimed at undergraduate students, new graduate students, and applied researchers.
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.
Inhaltsangabe
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
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
Rezensionen
'. . . 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
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