We have covered the following topics

1. Uses of regression models

a. Hypothesis testing

b. Prediction

2. Assumptions required for OLS estimator to be BLUE

3. Hypothesis testing:

a. t- tests (both one-sided and two-sided)

b. F-Tests (tests that coefficients are jointly zero and tests involving linear combinations of the coefficients)

c. Chi-Squared tests

4. The Types of Specification Error

5. Consequences of including an irrelevant variable

6. Consequences of excluding a relevant variable

7. Heteroskedasticity

a. How heteroskedasticity might arise

b. The consequences of estimating a heteroskedastic model with OLS

c. TestsGoldfeld-Quandt

White

LaGrange Multiplier Tests (Models 1, 2, and 3)d. Corrections

Multiplicative

Model 1

Model 2

Model 3

White’s (We didn’t give an explicit function for this one)

8. Autocorrelation

a. Assumptions required for estimators to be BLUE.

b. Assessing potential bias of an estimator.

c. Consequences of ignoring serial correlation and estimating with OLS.

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