**We have covered the following topics in the course:**

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. Testsi. Goldfeld-Quandt

ii. White's

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

i. Multiplicative:

ii. Model 1:

iii. Model 2:

iv. Model 3:

v. 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.

**Topics covered after Midterm 1:**

d. Tests for serial correlation

i. The Durbin-Watson test.

ii. Durbin's h-test.

iii. The Breusch-Godfrey test for higher order serial correlation.e. The CORC procedure

f. Corrections for higher-order serial correlation and search procedures

i. Estimating a model with higher order serial correlation using a generalized CORC procedure.

ii. Hildreth-Lu search procedure.

9. Stochastic Regressors and Measurement Errors

a. Assessing the bias and consistency of an estimator

b. Errors in variablesi. Consequences of estimating with OLS (differences in mismeasurement. of the dependent variable and the independent variables).

ii. Application of errors in variables: Friedman's Permanent Income Hypothesis.c. Instrumental variable estimation

i. What is an instrument.

ii. How is IV performed?

iii. Show how IV estimation can solve the problem of correlation of the right-hand side variables with the error term.

10. Simultaneous equation models

a. Structural equations (behavioral, identities, equilibrium conditions, technical equations) and reduced form equations. Endogenous, exogenous, and predetermined variables.

b. Consequences of ignoring simultaneity, i.e. demonstrate simultaneity bias.

c. Underidentified models, exactly identified models, and overidentified models

**Topics covered after Midterm 2:**

d. Estimation by 2SLS

11. Multicollinearity

a. What is multicollinearity and how does it affect OLS estimates and standard errors?

b. Detection of multicollinearity

c. What to do for perfect and imperfect multicollinearity.

12. Specification tests

a. LM test for adding a variable to a model (with and without endogeneity)

13. Qualitative and limited dependent variables

a. Linear probability model

i. description of model, problems, and estimation

b. Probit model

i. description of model and estimation

c. Logit model

i. description of model, attractive properties, and estimation

d. Limited dependent variables

i. description of the model, OLS when the dependent variable is limited, and estimation

14. Maximum likelihood

a. Brief description of what maximum likelihood estimation does.

b. properties of maximum likelihood estmators.

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