1. Regression Analysis

2. Introduction Derive the α and β Assess the use of the T-statistic
Discuss the importance of the Gauss-Markov assumptions
Describe the problems associated with autocorrelation, how to measure it and possible remedies
Introduce the problem of heteroskedasicity

3. Values and Fitted Values

4. Deriving the α and β
The aim of a least squares regression is to minimize the distance between the regression line and error terms (e).

5. The Constant

6. The Slope Coefficient (β)

7. T-test
When conducting a t-test, we can use either a 1 or 2 tailed test, depending on the hypothesis
We usually use a 2 tailed test, in this case our alternative hypothesis is that our variable does not equal 0. In a one tailed test we would stipulate whether it was greater than or less than 0.
Thus the critical value for a 2 tailed test at the 5% level of significance is the same as the critical value for a 1 tailed test at the 2.5% level of significance.

8. T-test
We can also test whether our coefficient equals 1.

9. Gauss-Markov Assumptions
There are 4 assumptions relating to the error term.
The first is that the expected value of the error term is zero
The second is that the error terms are not correlated
The third is that the error term has a constant variance
The fourth is that the error term and explanatory variable are not correlated.

10. Gauss-Markov assumptions
More formally we can write them as:

11. Additional Assumptions
There are a number of additional assumptions such as normality of the error term and n (number of observations) exceeding k (the number of parameters).
If these assumptions hold, we say the estimator is BLUE

12. BLUE Best or minimum variance Linear or straight line
Unbiased or the estimator is accurate on average over a large number of samples.
Estimator

13. Consequences of BLUE
If the estimator is not BLUE, there are serious implications for the regression, in particular we can not rely on the t-tests.
In this case we need to find a remedy for the problem.

14. Autocorrelation
Autocorrelation occurs when the second Gauss-Markov assumption fails.
It is often caused by an omitted variable
In the presence of autocorrelation the estimator is not longer Best, although it is still unbiased. Therefore the estimator is not BLUE.

15. Durbin-Watson Test This tests for 1st order autocorrelation only
In this case the autocorrelation follows the first-order autoregressive process

16. Durbin-Watson Test- decision frameworkdl du 2 4
4-du
b-dl
Zone of indecision

17. DW Statistic
The DW test statistic lies between 0 and 4, if it lies below the dl point, we have positive autocorrelation. If it lies between du and 4-du, we have no autocorrelation and if above 4-dl we have negative autocorrelation.
The dl and du value can be found in the DW d-statistic tables (at the back of most text books)

18. Lagrange Multiplier (LM) Statistic
Tests for higher order autocorrelation
The test involves estimating the model and obtaining the error term .
Then run a second regression of the error term on lags of itself and the explanatory variable: (the number of lags depends on the order of the autocorrelation, i.e. second order)

19. LM Test
The test statistic is the number of observations multiplied by the R-squared statistic.
It follows a chi-squared distribution, the degrees of freedom are equal to the order of autocorrelation tested for (2 in this case)
The null hypothesis is no autocorrelation, if the test statistic exceeds the critical value, reject the null and therefore we have autocorrelation.

20. Remedies for Autocorrelation
There are 2 main remedies:
The Cochrane-Orcutt iterative process
An unrestricted version of the above process

21. Heteroskedasticity
This occurs when the variance of the error term is not constant
Again the estimator is not BLUE, although it is still unbisased it is no longer Best
It often occurs when the values of the variables vary substantially in different observations, i.e. GDP in Cuba and the USA.

22. Conclusion
The residual or error term is the difference between the fitted value and actual value of the dependent variable.
There are 4 Gauss-Markov assumptions, which must be satisfied if the estimator is to be BLUE
Autocorrelation is a serious problem and needs to be remedied
The DW statistic can be used to test for the presence of 1st order autocorrelation, the LM statistic for higher order autocorrelation.