Econometrics Final Exam Questions with Verified Solutions Latest Version 2024-2025
How does omitting a relevant variable from a regression model affect the estimated coefficient of other
variables in the model? - Answers They are bias, and the bias can be negative or positive
When collinear variables are included in an econometric model coefficient estimates are - Answers
unbiased, but they have larger standard errors
If your regression results show a high R2, adj R2, and a significant F-test, but low t values for the
coefficients, what is the most likely cause? - Answers collinearity
Which of the following is true of heteroskedasticity?
a. Heteroskedasticty causes inconsistency in the Ordinary Least Squares estimators.
b. Population R2 is affected by the presence of heteroskedasticty.
c. The Ordinary Least Square estimators are not the best linear unbiased estimators if
heteroskedasticity is present.
d. It is not possible to obtain F statistics that are robust to heteroskedasticity of an unknown form. -
Answers C
Consider the following regression model: yi = B0 + B1xi + ui. If the first four Gauss-Markov assumptions
hold true, and the error term contains heteroskedasticity, then _____. - Answers Var(ui|xi) = (sigma) i^2
A regression model suffers from functional form misspecification if - Answers the interraction term is
ommitted
when is a proxy variable used? - Answers data on a key independent variable is unavialable
measurement error - Answers observed value of variable differs from actual value
endogenous sample selection - Answers sample selection based on the dependent variable
The method of data collection in which the population is divided into nonoverlapping, exhaustive groups
is called - Answers stratisfied sampling
key difference between time series and cross sectional data - Answers tie series is based on temporal
ordering
The sample size for a time series data set is the number of: - Answers time periods over which we
observe the variables of interest
A static model is postulated when - Answers a change in the independent variable at time 't' is believed
to have an immediate effect on the dependent variable.
, If an explanatory variable is strictly exogenous it implies that: - Answers the variable cannot react to
what has happened to the dependent variable in the past.
A seasonally adjusted series is one which - Answers has seasonal factors removed from it.
What should be the degrees of freedom (df) for fixed effects estimation if the data set includes 'N' cross
sectional units over 'T' time periods and the regression model has 'k' independent variables? - Answers
NT-N-k
Which of the following types of variables cannot be included in a fixed effects model? - Answers Time-
constant independent variable
The difference between the LPM model and the logit and probit models is that - Answers the LPM
assumes constant marginal effects for all the independent variables, while the logit and probit models
imply diminishing magnitudes of the partial effects.
Errors-in-variables bia - Answers arises when an independent variable is measured imprecisely
cov (uit, uis | Xit, Xis = 0 for t ≠ s means that - Answers conditional on the regressors, the errors are
uncorrelated over time.
Panel data is also called - Answers longitudinal data
The main advantage of using panel data over cross sectional data is that it - Answers allows you to
control for some types of omitted variables without actually observing them
In panel data, the regression error
A) is likely to be correlated over time within an entity
B) should be calculated taking into account heteroskedasticity but not autocorrelation
C) only exists for the case of T > 2
D) fits all of the three descriptions above - Answers A
A given time-series is said to have a time-trend if - Answers the data trend upward or downward over
time
You can control for a potential time-trend in your data by including a _____ in your regression. -
Answers variable that increases by 1 for each successive time-period
You can perform forecasting by - Answers using the results of regression analysis to predict the value of
the dependent variable will be at some point in the future.
How does omitting a relevant variable from a regression model affect the estimated coefficient of other
variables in the model? - Answers They are bias, and the bias can be negative or positive
When collinear variables are included in an econometric model coefficient estimates are - Answers
unbiased, but they have larger standard errors
If your regression results show a high R2, adj R2, and a significant F-test, but low t values for the
coefficients, what is the most likely cause? - Answers collinearity
Which of the following is true of heteroskedasticity?
a. Heteroskedasticty causes inconsistency in the Ordinary Least Squares estimators.
b. Population R2 is affected by the presence of heteroskedasticty.
c. The Ordinary Least Square estimators are not the best linear unbiased estimators if
heteroskedasticity is present.
d. It is not possible to obtain F statistics that are robust to heteroskedasticity of an unknown form. -
Answers C
Consider the following regression model: yi = B0 + B1xi + ui. If the first four Gauss-Markov assumptions
hold true, and the error term contains heteroskedasticity, then _____. - Answers Var(ui|xi) = (sigma) i^2
A regression model suffers from functional form misspecification if - Answers the interraction term is
ommitted
when is a proxy variable used? - Answers data on a key independent variable is unavialable
measurement error - Answers observed value of variable differs from actual value
endogenous sample selection - Answers sample selection based on the dependent variable
The method of data collection in which the population is divided into nonoverlapping, exhaustive groups
is called - Answers stratisfied sampling
key difference between time series and cross sectional data - Answers tie series is based on temporal
ordering
The sample size for a time series data set is the number of: - Answers time periods over which we
observe the variables of interest
A static model is postulated when - Answers a change in the independent variable at time 't' is believed
to have an immediate effect on the dependent variable.
, If an explanatory variable is strictly exogenous it implies that: - Answers the variable cannot react to
what has happened to the dependent variable in the past.
A seasonally adjusted series is one which - Answers has seasonal factors removed from it.
What should be the degrees of freedom (df) for fixed effects estimation if the data set includes 'N' cross
sectional units over 'T' time periods and the regression model has 'k' independent variables? - Answers
NT-N-k
Which of the following types of variables cannot be included in a fixed effects model? - Answers Time-
constant independent variable
The difference between the LPM model and the logit and probit models is that - Answers the LPM
assumes constant marginal effects for all the independent variables, while the logit and probit models
imply diminishing magnitudes of the partial effects.
Errors-in-variables bia - Answers arises when an independent variable is measured imprecisely
cov (uit, uis | Xit, Xis = 0 for t ≠ s means that - Answers conditional on the regressors, the errors are
uncorrelated over time.
Panel data is also called - Answers longitudinal data
The main advantage of using panel data over cross sectional data is that it - Answers allows you to
control for some types of omitted variables without actually observing them
In panel data, the regression error
A) is likely to be correlated over time within an entity
B) should be calculated taking into account heteroskedasticity but not autocorrelation
C) only exists for the case of T > 2
D) fits all of the three descriptions above - Answers A
A given time-series is said to have a time-trend if - Answers the data trend upward or downward over
time
You can control for a potential time-trend in your data by including a _____ in your regression. -
Answers variable that increases by 1 for each successive time-period
You can perform forecasting by - Answers using the results of regression analysis to predict the value of
the dependent variable will be at some point in the future.
