Y2 - Introduction to Econometrics

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1. Week 2:
2. L4: OLS-simple liner regression hypothesis tests and confidence intervals
3. 4.1. Simple linear regression model: example; why need statistical inference
4. 4.2. Hypothesis tests in the linear regression model: two-sided hypothesis test; t-statistic, p-value
5. 4.3. Two-sided or one-sided test?
6. 4.4. Confidence intervals for coefficients of linear regression model
7. 4.5. Special case of homoskedasticity; heteroskedasticity-robust standard errors
8. 4.6. Important warning
9. Exercises 4: Emprical Exercise 5.3. of the book using Stata
10. L5: OLS-OVB, multiple linear regression, assumptions; goodness of fit
11. 5.1. Omitted varaible bias (OVB) and multiple regression model as possible solution for it
12. 5.2. Multiple regression model: interpretation, OLS estimator, predicted values and residuals
13. 5.3. Multiple regression model: Summary of assumptions
14. 5.4. Multiple regression model: Assumption 1, ZCM and weaker version- Conditional mean independence
15. 5.5. Multiple regression model: Assumption 4- No perfect multicollinearity; interpretation of categorical varaibles
16. 5.6. Multiple regression model: Sampling distribution of OLS estimator assumptions
17. 5.7. Multiple regression model: goodness of fit measures and warnings anout them
18. Exercises 5. Empirical Exercise 6.1. using Stata: OVB, OLS assumptions; measures of fit
19. L6: OLS-hypothesis tests, confidence intervals and model specifications
20. 6.1. Multiple regression model: Example smoking and income
21. 6.2. Multiple regression model: Hypothesis tests for a single coefficient; confidence intervals
22. 6.3. Multiple regression model: Tests of hypotheses about multiple coefficients
23. 6.4. Does our multiple regression model still suffer from omitted variables?
24. Exercise 6: Multiple regression model- examples F-tests in Stata