[Note: Previous midterms are here.]

1. Assumptions required for estimates to be BLUE

2. Hypothesis testing:

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

b. Joint hypotheses (F-Tests, Chi-square tests, etc.)i. Exclusion restrictions

ii. Linear combinations of parameters

3. Heteroskedasticity

a. What is heteroskedasticity?

b. How heteroskedasticity occur?

c. The consequences of estimating a heteroskedastic model with OLS

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

Model 1:

Model 2:

Model 3:ii. Goldfeld-Quandt test

iii. White's teste. Corrections/Estimation procedures

i. Feasible GLS

Model 1:

Model 2:

Model 3:iii. White’s correction

8. Autocorrelation

a. What is it and why might it occur?

b. Consequences of ignoring serial correlation and estimating with OLSi. Model with serially correlated errors (model 1 in class)

ii. Model including a lagged dependent variable (model 2 in class)

iii. Model with both a lagged dependent variable and serial correlated errors (model 3 in class)c. Tests for serial correlation

i. The Durbin-Watson test.

ii. Durbin's h-test.

iii. The Breusch-Godfrey test for higher order serial correlation.d. Corrections

i. Non-linear estimation

9. Testing for ARCH errors

10. Stochastic Regressors and Measurement Errors

a. Assessing the bias and consistency of an estimator

b. Errors in variablesi. Consequences of estimating with OLS when there are errors in measuring the right-hand side variables (i.e. errors in measuring the independent variables, the X's).

ii. Consequences of estimating with OLS when there are errors in measuring the dependent variable (i.e. in the measurement of Y).

## Comments