OVERVIEW

Part I - The Linear Regression Model

1. What is Econometrics?

2. Choosing Estimators: Intuition and Monte Carlo Methods

3. Linear Estimators and the GaussMarkov Theorem

4. BlueEstimators for the Slope and Intercept of a Straight Line

5. Residuals

6. Multiple Regression

Part II - Specification and Hypothesis Testing

7. Testing Single Hypotheses in Regression Models

8. Superfluous and Omitted Variables, Multicollinearityand Binary Variables

9. Testing Multiple Hypotheses

Part III - Further Topics in Regression

10. Heteroskedastic Disturbances

11. AutoregressiveDisturbances

12. Large Sample Properties Of Estimators: Consistencyand Asymptotic Efficiency

13. Instrumental Variables Estimation

14. Systems of Equations

15. Randomized Experimentsand Natural Experiments

16. Analyzing Panel Data

17. Forecasting

18. Stochastically Trending Variables

19. Logit and Probit Models: Truncated and Censored Samples

Statistical Appendix

WEB EXTENSION 1 USING CALCULUS AND ALGEBRA FOR THE SIMPLEST CASE: n = 3

WEB EXTENSION 2 LOCAL AVERAGE TREATMENT EFFECTS

WEB EXTENSION 3 GENERALIZED METHOD OF MOMENTS ESTIMATORS AND IDENTIFICATION

WEB EXTENSION 4 MAXIMUM LIKELIHOOD ESTIMATION

WEB EXTENSION 5 ESTIMATORS FOR SYSTEMS OF EQUATIONS

WEB EXTENSION 6 MULTIPLE COINTEGRATING RELATIONSHIPS

WEB EXTENSION 7 LOG-ODDS AND LOGIT MODELS: USING GROUPED DATA

WEB EXTENSION 8 MULTINOMIAL MODELS