[pdf file]

**Midterm 1 will cover the following topics:**

- The two uses of regression models
- The assumptions underlying the Guass-Markov Theorem
- The Guass-Markov Theorem and what BLUE means
- Reasons for an error in a regression model
- How t-tests and z-tests differ (when to use each one)
- Single hypothesis tests: z-tests and t-tests
- Multiple hypothesis tests: F-tests and Chi-Square tests
- [Should be able to do hypothesis tests with these four distributions]
- Functional form: changes in units
- Functional form: log-linear, semi-log, reciprocal, and log reciprocal models (see page 190 for a summary).
- Use of dummy variables in regression models
- Dummy variable trap
- Chow tests and dummy variables
- Piecewise linear regression
- Model selection criteria
- Types of specification errors
- Consequences of omitting a relevant variable
- Consequences of including an irrelevant variable
- LM test for adding variables to a regression model
- How to do an AIC test
- Consequences of errors in measuring y
- Consequences of errors in measuring the x variables.
- Perfect and imperfect multicollinearity
- Consequences of multicollinearity
- Detection of multicollinearity
- What to do about multicollinearity

**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.

**Material since Exam #2**

Chapter 12 [cont.]

- Use of lagged dependent variables to solve the autocorrelation problem

Simultaneous Equation Models (Chapter 18)

- Simultaneous equation models

- endogenous, exogenous, and predetermined variables
- structural models and reduced forms
- simultaneous equation bias

Identification (Chapter 19)

- Explaining identification intuitively with, say, a supply and demand model
- Under, exact, and overidentification
- Rules for identification

- Order condition explicitly
- Rank condition intuitively

Estimation of Simultaneous Equation Models (Chapter 20)

- Indirect least squares (ILS)

- Exact identification (works)
- Over or under identified (won't work)
- Two-stage least squares (2SLS)

- Exact or over identified (works)
~~Over or~~under identification (won't work)- Stage 1
- Stage 2
- Standard errors

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