QUIZ 4

Question 1

What is a dummy variable?

Answer 1

A useful device used in regression analysis

Question 2

Define “dummy variable.”

Answer 2

Variable that takes the value of 1 for some Observations to show the presence of an effect & 0 for he remaining observations

Question 3

What are the only 2 numbers a dummy variable can be?
1. 1& 0
2. 2 & 1
3 . 5 & 10

Answer 3

1 & 0

Question 4

What types of vars are nominal scale?

Answer 4

Gender, race, color, religion

Question 5

The category which no dummy var is assigend is know as the
1. exponential
2. base, benchmark
3. final, finalized

Answer 5

2. base, benchmark

Question 6

The intercept value represents the ______ of the benchmark category
1. sum
2. multiple
3. mean

Answer 6

3. mean

Question 7

What is the purpose of an interaction var?

Answer 7

To measure the interaction effect of multiple vars

Question 8

What regression equation is the dummy var most useful?
1. LOG-LOG
2. LINEAR - LOG
3. LOG-LINEAR

Answer 8

3. LOG-LINEAR

Question 9

How to avoid the dummy var trap?

Answer 9

DROP the dummy variable for the fourth quarter

Question 10

The OLS estimators of the parameter of the incorrect model are all ________ and _________

Answer 10

1.unbiased
2. consistent

Question 11

If you include irrelevant vars, this may _______ the _______ of the OLS estimators

Answer 11

1. increase
2. variance

Question 12

The only penalty we pay for the inclusion of the superfluous variable is that the estimated variances of the coefficients are
1. smaller
2. larger
3. insignificant

Answer 12

2. larger

This leads to our probability inferences about the parameters being less precise

Question 13

If the omitted variable is _______ with the included variable X1, that is the correlation coefficient between them is __________

Answer 13

1. correlated
2. nonzero

Question 14

The residuals estimated from the model must be ______
1. exact
2. specific
3.nonrandom
4. random

Answer 14

random

Question 15

What is an issue with R^2
1. can fall when more vars are added to the model
2. cannot fall when more vars are added to the model
3. cannot fall when less vars are added to the model

Answer 15

2. cannot fall when more vars are added to the model

Question 16

What must be the same for a comparison between vars to work?

Answer 16

the dependent var must be the same when comparing to different regression models

Question 17

One advantage of AIC is that it is useful for not only _________ but also ___________ forecasting performance of a regression model

Answer 17

1. in-sample
2. out-of-sample

Question 18

Does one want a model with higher or lower AIC?

Answer 18

LOWER AIC

Question 19

_______ refers to how symmetric the residuals are around zero
1. variation
2. skewness
3. probability

Answer 19

2. skewness

Question 20

Preferably symmetric residuals will have a skewness of
1. 0.99
2. 1
3. 0

Answer 20

3. 0

Question 21

Kurtosis refers to the ______ of the distribution
1. Variance
2. Peakedness
3. Average

Answer 21

2. Peakedness

Question 22

The Jarque-Bera (JB) test of normality is a test of the joint hypothesis that _ and _ are 0 and 3, respectively.

Answer 22

S - skewness
K - kurtosis