1. The Simple Linear Regression Model Specification and Estimation Hill et al Chs 3 and 4

2. Expenditure by households of a given income on food

3. Economic Model Assume that the relationship between income and food expenditure is linear: But, expenditure is random: Known as the regression function.

4. Econometric model

5. Combines the economic model with assumptions about the random nature of the data. Dispersion. Independence of y i and y j. x i is non-random.

6. Writing the model with an error term An observation can be decomposed into a systematic part: –the mean; and a random part:

7. Properties of the error term

8. Assumptions of the simple linear regression model

9. The error term Unobservable (we never know E(y)) Captures the effects of factors other than income on food expenditure: –Unobservered factors. –Approximation error as a consequence of the linear function. –Random behaviour.

10. Fitting a line

11. The least squares principle Fitted regression and predicted values: Estimated residuals: Sum of squared residuals:

12. The least squares estimators

13. Least Squares Estimates When data are used with the estimators, we obtain estimates. Estimates are a function of the y t which are random. Estimates are also random, a different sample with give different estimates. Two questions: –What are the means, variances and distributions of the estimates. –How does the least squares rule compare with other rules.

14. Expected value of b 2 Estimator for b 2 can be written: Taking expectations:

15. Variances and covariances

16. Comparing the least squares estimators with other estimators Gauss-Markov Theorem: Under the assumptions SR1-SR5 of the linear regression model the estimators b 1 and b 2 have the smallest variance of all linear and unbiased estimators of 1 and 2. They are the Best Linear Unbiased Estimators (BLUE) of 1 and 2

17. The probability distribution of least squares estimators Random errors are normally distributed: –estimators are a linear function of the errors, hence they a normal too. Random errors not normal but sample is large: –asymptotic theory shows the estimates are approximately normal.

18. Estimating the variance of the error term

19. Estimating the variances and covariances of the LS estimators