Chapter 1. Simple regression analysis
Chapter 3. Multiple regression analysis | Chapter 8. Stochastic regressors and measurement errors | Chapter 9 . Simultaneous equations estimation | Identification | Chapter 10. Binary choice models, tobit model and ML estimation | Chapter 11. Models using time-series data | Chapter 12. Autocorrelation | Chapter 13. Introduction to nonstationary time series |
Review Chapter
- Read the chapter as necessary, to refresh your knowledge of statistics.
Chapter 1. Simple regression analysis
- Explain mathematically 5 reasons for inclusion of the error term in the regression model
- Write down the normal equations and derive the OLS estimators (the general case).
- Explain the difference between the regression model and fitted line, true and estimated coefficients, error term and residual and between the two decompositions of the dependent variable.
- How do changes in the units of measurement affect the estimated coefficients? What is meant by demeaning and why may it be desirable?
- Do Exercise 1.4. In the table of Ex. 1.4 find TSS, RSS, ESS, and interpret p-value of the F statistics, , and adjusted . Find the estimates of the coefficients, their standard errors, t statistics, indicate the corresponding formulas. Find and interpret p-values and confidence intervals. Answer the same questions using Eviews output.
- Explain geometrically why the mean value of residuals is zero and the sample correlation between the observations of X and the residuals is zero.
- What happens to the slope estimator if the intercept is underestimated?
- Assuming orthogonality relations known, derive the decomposition of TSS. Give two equivalent definitions of . Why is maximizing the same thing as minimizing RSS? What happens to when a new explanatory variable is added?
- Prove the alternative interpretation of .
- 1.6, 1.7, 1.16*, 1.17*, 1.18*, 1.23* (starred exercises to be presented at the board, the others to be solved but presented only as needed)
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