Final

Question 1

We do not need the normality of the error term assumption to perform valid statistical inference if the other multiple linear regression model assumptions hold and we have a large sample.

Answer 1

True

Question 2

Heteroskedasticity causes the OLS estimator to be biased.

Answer 2

False

Question 3

Heteroskedasticity causes the OLS estimator to be inconsistent.

Answer 3

False

Question 4

Heteroskedasticity causes the usual estimator of the variance of the OLS estimator to be inconsistent.

Answer 4

True

Question 5

Heteroskedasticity-robust standard errors are valid only if the sample size is large.

Answer 5

True

Question 6

Heteroskedasticity-robust standard errors are always larger than the usual standard errors.

Answer 6

False

Question 7

When the error term in a regression model is heteroskedastic, the OLS estimator is not the best linear unbiased estimator (BLUE).

Answer 7

True

Question 8

With a large sample size, heteroskedasticity-robust standard errors are valid even if the error term is homoskedastic.

Answer 8

True

Question 9

Classical measurement error in the dependent variable does not cause bias in the OLS estimator, although it does increase the variance of the OLS estimator.

Answer 9

True

Question 10

Under the classical measurement error assumption, measurement error in an explanatory variable causes attenuation bias.

Answer 10

True

Question 11

If x is correlated with x* and if x is uncorrelated with the error term, u, then we say that x is a good proxy for x*

Answer 11

False

Question 12

The F test is not useful in detecting functional form misspecification. Instead, one should use RESET or the Davidson-MacKinnon test.

Answer 12

False

Question 13

Functional form misspecification is when the model does not properly account for the relationship between the dependent and explanatory variables, often because the appropriate explanatory variables are not observed.

Answer 13

False

Question 14

RESET is useful in detecting functional form misspecification as well as general omitted variable bias.

Answer 14

False

Question 15

Removes serial correlation via an iterative process

Answer 15

Cochrone Orcutt / Prais Winston

Question 16

Tests for Functional Form Misspecification

Answer 16

Ramsey RESET

Question 17

Tests for Heteroskedasticity

Answer 17

Breusch Pagan Test or White Test

Question 18

Highly persistent series do not give biased estimates.

Answer 18

False

Question 19

Eliminates high persistence in a time series

Answer 19

First Difference

Question 20

Any result from a highly persistent series is spurious.

Answer 20

True

Question 21

Tests for unit root

Answer 21

Dicky Fuller

Question 22

Finding a relationship between 2 or more trending variables simple because each is growing over time.

Answer 22

Spurious Regression Problem

Question 23

The null hypothesis of the dicky fuller test

Answer 23

unit root

Question 24

How to correct for a spurious regression problem in a time series

Answer 24

Add a time trend

Question 25

How do you account for seasonality

Answer 25

Include seasonal dummy variables and joint f test all seasonal dummy variables to check for significance.

Question 26

The long run propensity (LRP) in a finite distributed lag model is the average of all the coefficients on the included lags of the variable of interest plus the value of the contemporaneous variable of interest.

Answer 26

False

Question 27

The Dickey-Fuller test can be used to determine if there is evidence that the specified time series is not highly persistent.

Answer 27

True

Question 28

What would be an appropriate procedure to correct the standard errors when serial correlation is present in a time series regression model?

Answer 28

Cochrane-Orcutt Estimation

Question 29

The Cochrane-Orcutt estimation procedure should be used when regressing a highly persistent time series on another highly persistent time series in order to obtain unbiased parameter estimates.

Answer 29

False. So what is Conchrane Orcutt used for?

Question 30

Serially correlated errors cause the OLS estimator to be biased and inconsistent.

Answer 30

False

Question 31

Regressing a highly persistent time series on another highly persistent time series produces spurious results.

Answer 31

True

Question 32

First differencing can be used to render a highly persistent time series weakly dependent.

Answer 32

True

Question 33

Both first-differenced estimation and fixed-effects estimation can be used to estimate causal effects if the unobserved factors that are correlated with the independent variable of interest change over time.

Answer 33

False

Question 34

Fixed-effects estimation can be used to estimate causal effects if the unobserved factors that are correlated with the dependent variable of interest are time invariant.

Answer 34

True

Question 35

If the average value of the outcome variable is different for the treated and control groups before the treatment, difference-in-differences estimation will not be able to provide an unbiased estimate of the effect.

Answer 35

False

Question 36

The validity of difference-in-differences estimation depends on the assumption that the change in the treated and control groups would have been the same had it not been for the treatment.

Answer 36

True

Question 37

Under which conditions is the difference-in-differences estimator not able to provide an unbiased estimate of the effect?

Answer 37

Some other factor that changes over time affects the outcome for only the treated group.

Question 38

With panel data, estimation in first differences and fixed-effects estimation are computationally identical.

Answer 38

False

Question 39

If the dependent variable is a binary variable, the error term is obviously not normally distributed. This may result in biased OLS estimates.

Answer 39

False

Question 40

To model if there are increasing or decreasing returns to a particular independent variable one should include an interaction term.

Answer 40

False

Question 41

What is the null hypothesis for the Breusch-Pagan Test?

Answer 41

Ho: homoskedasticity