This review is intended to give
you a general idea of what may appear on the exam. The exam will cover material from the
assigned readings and material that was presented in the lecture.
Please bring a calculator.
Ch 6
Standardized coefficients
Logarithmic functional forms
Adjusted R-squared
Ch 7 Dummy Variables
Table 7.1
A single dummy independent
variable
Equation
7.1
Figure
7.1
Example
7.1
Using dummy variables for multiple
categories
Allowing for different slopes
Equation
7.16
Equation
7.17
Figure
7.2
Ch 8 Heteroskedasiticity
Testing for heteroskedasticity
Equation
8.14
The
White Test
Equation 8.19
Equation
8.20
Weighted Least Squares Estimation
Pp270-274
FGLS pp276-280
Ch 9
RESET as a General Test for
Functional Form misspecification
Pp292-293
Using Lagged Dependent Variables
as Proxy Variables
Pp300-302
Example
9.4
Missing Data
Pp309-310
Outliers and influential
observations
SPSS:
Standardized residual
Autocorrelation
Ch 10 Serial correlation pp333-334
Ch 11 The Durbin-Watson Test
pp397-399
Ch 12 Differencing and Serial Correlation pp409-410
Ch 17 Limited dependent variable
models
Pp553-558
Figure
17.1
Interpreting the logit model pp559-565
Pseudo R squared
Calculating predicted
probabilities
II. The
following instructions will be provided
FGLS
1. Run the regression of y on
and obtain the residuals
.
2. Create ![]()
3. Run the regression of
on
and obtain the
predicted (fitted) values, ![]()
4. Caluculate h by exponentiating the fitted values, ![]()
5. Estimate the equation
by WLS, using weights 1/h
Durbin-Watson Test
The null hypothesis: No autocorrelcation
d < dL Reject the null
hypothesis
d
> dU Fail
to reject the null hypothesis
dL
<d < dU Inconclusive
III. The following formulae will be provided. The tables for t, F, and
Durbin-Watson will also be provided
T test:
(4.5)
A single dummy independent
variable
(7.1)
Interactions involving dummy
variables
(7.16)
Test for Hetroskedasticity
(8.20)
Testing Multiple Linear
Restrictions
![]()
![]()
RESET
Restricted:
![]()
Unrestricted:
![]()
Logit
Logarithmic function
![]()