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
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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