Using econometrics : a practical guide / A.H. Studenmund, Henry J. Cassidy.

Contents
1. An Overview of Regression Analysis
1.1 What is Econometrics?
1.2 What is regression analysis
1.3 A simple Example of Regression
1.4 Summary and Exercises

2. Ordinary Least Squares
2.1 Estimating the Coefficients of the model with OLS
2.2 Evaluating the overall fit of Estimated model
2.3 An Example of the miuse of R2
2.4 Summary and Exercises

3. Learning to use Regression Analysis
3.1 The measurement of variables
3.2 Steps in Applied Regression
3.3 A more Complete Example of Applied Regression
3.4 Summary and Exercise

4. The Classical Model
4.1 The Classical Assumption
4.2 The Normal Distribution of the Error Term
4.3 The Sampling Distribution
4.4 The Gauss-Markov Theorem and the properties of OLS Estimators etc

5. Hypothesis Testing and the t-Test
5.1 What is Hypothesis Testing
5.2 The t-Test
5.3 Examples of One-sided t-Tests
5.4 Examples Two-sided t-Tests etc.

6. Specification: Choosing the Independent Variables
6.1 Omitted Variables
6.2 Irrelevant Variables
6.3 Illustration of misuse of the t-Test
6.4 Specification searches etc.

7. Specification: Choosing Functional Form
7.1 Alternative Functional Forms
7.2 problems with incorrect Functional Forms
7.3 Slope Dummies and Jack-knifing
7.4 The use and interpretation of the constant Term etc.

8. Multicollinearity
8.1 Perfect vs. Imperfect Multicollinearity
8.2 The Consequences of multicollinearity
8.3 The Detection of multicollinearity
8.4 Remedies for multicollinearity etc.

9. Serial Correlation
9.1 Pure vs. Impure Serial correlation
9.2 The Consequences of Serial Correlation
9.3 The Durbin-Watson d Test
9.4 Generalized Least Squares

10. Heteroskedasticity
10.1 Pure Vs. Impure Heteroskedasticity
10.2 The consequences of Heteroskedastcity
10.3 Testing for Heteroskedasticity
10.4 Remedies for Heteroskedasticity etc.

11. A regression user's Handbook
11.1 A regression user's Checklist
11.2 A regression user's Guide
11.3 The Ethical Econometrician
11.4 An interactive regression Learning Example etc.

12. Simultaneous Equations
12.1 Structural and Reduced-Form Equations
12.2 The Bias of Ordinary Least (OLS)
12.3 Two-stage Least Squares(2SLS)
12.4 The Identification Problem etc.

13. Forecasting?
13.1 What is forecasting?
13.2 More Complex Forecasting
13.3 Forecasting with ARIMA, or Time Series Analysis
13.4 Summary and Exercise

Includes bibliographical references and index.