1. ECONOMETRICS I
CHAPTER 5: TWO-VARIABLE REGRESSION: INTERVAL ESTIMATION AND HYPOTHESIS TESTING
Textbook: Damodar N. Gujarati (2004) Basic Econometrics, 4th edition, The McGraw-Hill Companies

2. 5.2 Interval Estimation: Some Basic Ideas
Because of sampling fluctuations, a single estimate is likely to differ from the true value, although in repeated sampling its mean value is expected to be equal to the true value.

3. 5.2 Interval Estimation: Some Basic Ideas
In statistics the reliability of a point estimator is measured by its standard error. Therefore, instead of relying on the point estimate alone, we may construct an interval around the point estimator, say within two or three standard errors on either side of the point estimator, such that this interval has, say, 95 percent probability of including the true parameter value. This is roughly the idea behind interval estimation.

4. 5.2 Interval Estimation: Some Basic Ideas

5. 5.2 Interval Estimation: Some Basic Ideas
Confidence coefficient= 0.95 = 95 %
Level of significance= 0.05 = 5 %
If α = 0.05, or 5 percent, (5.2.1) would read: The probability that the (random) interval shown there includes the true β2 is 0.95, or 95 percent. The interval estimator thus gives a range of values within which the true β2 may lie.

6. 5.2 Interval Estimation: Some Basic Ideas
It is very important to know the following aspects of interval estimation:

7. 5.2 Interval Estimation: Some Basic Ideas

8. 5.2 Interval Estimation: Some Basic Ideas

9. 5.3 CONFIDENCE INTERVALS FOR REGRESSION COEFFICIENTS β1 AND β2
CONFIDENCE INTERVAL FOR β2
With the normality assumption for ui, the OLS estimator is normally distributed.

10. CONFIDENCE INTERVAL FOR β2
We can use the normal distribution to make probabilistic statements about β2 provided the true population variance σ2 is known. If σ2 is known, an important property of a normally distributed variable with mean μ and variance σ2 is that the area under the normal curve between μ ± σ is about 68 percent, that between the limits μ ± 2σ is about 95 percent, and that between μ ± 3σ is about 99.7 percent.

11. CONFIDENCE INTERVAL FOR β2

12. CONFIDENCE INTERVAL FOR β2
The t value in the middle of this double inequality is the t value given by (5.3.2) and where tα/2 is the value of the t variable obtained from the t distribution for α/2 level of significance and n − 2 df. it is often called the critical t value at α/2 level of significance.

13. CONFIDENCE INTERVAL FOR β2

14. CONFIDENCE INTERVAL FOR β2

15. CONFIDENCE INTERVAL FOR β2

16. CONFIDENCE INTERVAL FOR β1

17. CONFIDENCE INTERVAL FOR β1

18. 5.4 CONFIDENCE INTERVAL FOR σ2

19. 5.4 CONFIDENCE INTERVAL FOR σ2

20. 5.4 CONFIDENCE INTERVAL FOR σ2

21. 5.4 CONFIDENCE INTERVAL FOR σ2

22. 5.5 HYPOTHESIS TESTING: GENERAL COMMENTS

23. 5.5 HYPOTHESIS TESTING: GENERAL COMMENTS
Confidence interval approach
Test of significance approach
Both these approaches predicate that the variable (statistic or estimator) under consideration has some probability distribution and that hypothesis testing involves making statements or assertions about the value(s) of the parameter(s) of such distribution.

24. 5.6 HYPOTHESIS TESTING: THE CONFIDENCE-INTERVAL APPROACH
95 % CI for Beta-2 is (0.4268, ).

25. In statistics, when we reject the null hypothesis, we say that our finding is statistically significant. On the other hand, when we do not reject the null hypothesis, we say that our finding is not statistically significant.
Two-sided test vs. one-sided test
→ two-sided test
→ one-sided test

26. 5.7 HYPOTHESIS TESTING: THE TEST-OF-SIGNIFICANCE APPROACH
Broadly speaking, a test of significance is a procedure by which sample results are used to verify the truth or falsity of a null hypothesis. The key idea behind tests of significance is that of a test statistic (estimator) and the sampling distribution of such a statistic under the null hypothesis. The decision to accept or reject H0 is made on the basis of the value of the test statistic obtained from the data at hand.

27. 5.7 HYPOTHESIS TESTING: THE TEST-OF-SIGNIFICANCE APPROACH
This variable follows the t distribution with n−2 df.

28. 5.7 HYPOTHESIS TESTING: THE TEST-OF-SIGNIFICANCE APPROACH
is the value of β2 under H0 and where −tα/2 and tα/2 are the values of t (the critical t values) obtained from the t table for (α/2) level of significance and n − 2 df.

29. 5.7 HYPOTHESIS TESTING: THE TEST-OF-SIGNIFICANCE APPROACH

30. 5.7 HYPOTHESIS TESTING: THE TEST-OF-SIGNIFICANCE APPROACH

31. 5.7 HYPOTHESIS TESTING: THE TEST-OF-SIGNIFICANCE APPROACH

32. 5.7 HYPOTHESIS TESTING: THE TEST-OF-SIGNIFICANCE APPROACH

33. 5.7 HYPOTHESIS TESTING: THE TEST-OF-SIGNIFICANCE APPROACH
Since we use the t distribution, the preceding testing procedure is called appropriately the t test. In the language of significance tests, a statistic is said to be statistically significant if the value of the test statistic lies in the critical region. In this case the null hypothesis is rejected. By the same token, a test is said to be statistically insignificant if the value of the test statistic lies in the acceptance region. In this situation, the null hypothesis is not rejected. In our example, the t test is significant and hence we reject the null hypothesis.

34. 5.7 HYPOTHESIS TESTING: THE TEST-OF-SIGNIFICANCE APPROACH
To test this hypothesis, we use the one-tail test (the right tail), as shown in Figure 5.5.
The test procedure is the same as before except that the upper confidence limit or critical value now corresponds to tα = t0.05, that is, the 5 percent level. As Figure 5.5 shows, we need not consider the lower tail of the t distribution in this case.
CI = (- ∞, )

36. TABLE 5.1 (page 133)

37. Testing the Significance of σ2: The χ2 Test

38. Testing the Significance of σ2: The χ2 Test

39. The Meaning of “Accepting” or “Rejecting” a Hypothesis

40. The Exact Level of Significance: The p Value
Once a test statistic (e.g., the t statistic) is obtained in a given example, why not simply go to the appropriate statistical table and find out the actual probability of obtaining a value of the test statistic as much as or greater than that obtained in the example? This probability is called the p value (i.e., probability value), also known as the observed or exact level of significance or the exact probability of committing a Type I error. More technically, the p value is defined as the lowest significance level at which a null hypothesis can be rejected.

41. 5.9 REGRESSION ANALYSIS AND ANALYSIS OF VARIANCE
TSS = ESS + RSS
A study of these components of TSS is known as the analysis of variance (ANOVA) from the regression viewpoint.

42. 5.9 REGRESSION ANALYSIS AND ANALYSIS OF VARIANCE

43. 5.9 REGRESSION ANALYSIS AND ANALYSIS OF VARIANCE

44. 5.9 REGRESSION ANALYSIS AND ANALYSIS OF VARIANCE

45. 5.11 REPORTING THE RESULTS OF REGRESSION ANALYSIS

46. 5.11 REPORTING THE RESULTS OF REGRESSION ANALYSIS
In Eq. (5.11.1) the figures in the first set of parentheses are the estimated standard errors of the regression coefficients, the figures in the second set are estimated t values computed from (5.3.2) under the null hypothesis that the true population value of each regression coefficient individually is zero (e.g., = ÷ ), and the figures in the third set are the estimated p values. Thus, for 8 df the probability of obtaining a t value of or greater is and the probability of obtaining a t value of or larger is about