OVERVIEWPart I - The Linear Regression Model1. What is Econometrics?2. Choosing Estimators: Intuition and Monte Carlo Methods3. Linear Estimators and the Gauss-Markov Theorem4. Blue Estimators for the Slope and Intercept of a Straight Line5. Residuals6. Multiple Regression Part II - Specification and Hypothesis Testing 7. Testing Single Hypotheses in Regression Models8. Superfluous and Omitted Variables, Multicollinearity and Binary Variables9. Testing Multiple HypothesesPart III - Further Topics in Regression 10. Heteroskedastic Disturbances 11. Autoregressive Disturbances12. Large Sample Properties Of Estimators: Consistency and Asymptotic Efficiency13. Instrumental Variables Estimation14. Systems of Equations15. Randomized Experiments and Natural Experiments16. Analyzing Panel Data17. Forecasting18. Stochastically Trending Variables19. 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