autocorrelation Flashcards
When some explanatory variables of a regression model are strongly correlated,
this phenomenon is called autocorrelation.
a. True
b. False
b
How can autocorrelation occur?
a. If you omit an important variable with some degree of persistence
b. When you have narrower confidence intervals
c. When the model is well specified
d. A and B are correct
a
Which of the following could be an example of autocorrelation
a. Including occupation and income in your regression
b. Financial crisis on the housing market
c. Regressing crime and police in the same model
d. Having income as the dependent variable, age as the independent variable
and not including education
b
Given the following equation, what values of 𝝆 represent no autocorrelation?
𝑌௧ = 𝛽 + 𝛽ଵ௧𝑋ଵ௧+ 𝜀௧ ; 𝜀௧= 𝜌 𝜀௧ିଵ + 𝑢௧
a. 𝜌 = 1
b. 𝜌 = 0
c. 𝜌 > 0
d. 𝜌 < 0
b
true or False: A sequence of error terms that tend to alternate between positive
and negative is characteristic of positive autocorrelation.
a. True
b. False
b
Which of the following regressions constitutes the regression used to conduct the
Durbin Watson test?
a. 𝑦 = 𝛽 + 𝛽ଵ𝑥 + 𝜖
b. 𝜖ଶ = 𝛽 + 𝛽ଵ𝑥 + 𝑢
c. 𝜖௧ = 𝜌𝜖௧ିଵ + 𝑢௧
d. 𝜖௧ = 𝛽 + 𝛽ଵ𝑥௧ + 𝑢
c
True or False: One reason autocorrelation is problematic is because it causes the
estimates for the coefficients to become biased.
a. True
b. False
b
If two variables are completely uncorrelated, they will have a
Variance Inflation Factor (VIF) that is:
A. Negative
B. Between zero and one
C. Equal to one
D. Greater than one
c
he following equation is best classified as an example of?
𝑌௧ = 𝛽 + 𝛽ଵ௧𝑋ଵ௧ + 𝜀௧ ; 𝜀௧ = 𝜌 𝜀௧ିଵ + 𝑢௧
a. Multiple Regression
b. Collinearity
c. Heteroskedasticity
d. Autocorrelation
d
Which of the following violates the assumptions of regression analysis?
A) The error term is normally distributed.
B) The error term has a zero mean.
C) The error term is correlated with an explanatory variable.
D) The error term has a constant variance
c
When confronted with multicollinearity, a good remedy is to ________ if
we can justify its redundancy.
A) add one more collinear variable
B) drop one of the collinear variables
C) remove both the collinear variables
D) add as many collinear variables as possible
b
