Here's an outline of the material for the second exam:

Heteroskedasticity (Chapter 11)

- How it is defined
- How it might arise
- Effect on estimator, test statistics, etc. if OLS used
- Testing for Heteroskedasticity

- Graphs (suggestive only)
- La Grange Multiplier tests

- (i) Breusch-pagan
- (ii) Glesjer
- (iii) Park
- Goldfeld-Quandt test
- White’s test (recommended if N large enough)

- Estimation procedures

- White’s correction
- GLS
- FGLS (Feasible GLS)

- (i) Known proportional factor
- (ii) Breusch-pagan
- (iii) Glesjer
- (iv) Park

Autocorrelation (Chapter 12)

- How it is defined and how it expresses itself in a regression model

- Show how the corr(u
_{t }, u_{t-s}) changes with s- How serial correlation might arise
- Effect on estimator, test statistics, etc. if OLS used
- Testing for autocorrelation

- Graphs (suggestive only)
- Durbin-Watson Test

- Show statistic is between 0 and 4
- Advantages and disadvantages relative to Breusch-Godfrey
- Breusch-Godfrey LM test

- Estimation procedures

- GLS
- FGLS (Feasible GLS)

- Cochrane-Orcutt
- Grid search
- Note: I didn't quite finish this section and will add more after the exam.

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