In a lin-log model relating electricity consumption to log income, β=15. What does this mean?

A 1% increase in income increases consumption by 0.15 units.

What is multicollinearity?

Two or more independent variables are highly correlated.

The coefficient of determination R2 can only be used for model comparison if ...

The models have an intercept., The number of the x-variables is exactly the same., The y-variable of the models is exactly the same.

After running a logit model, you find the coefficient for "hours studied" is 0.5. What can you conclude?

The more hours studied, the higher the probability to pass the exam.

Which of the following is NOT an assumption of the classical linear regression model?

The error terms are heteroscedastic.

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Econometric Essentials

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1
Which ARIMA process is represented by the following equation?
ARIMA(2,2,1)
ARIMA(1,2,2)
ARIMA(1,1,1)
ARIMA(1,2,1)
2
What does d stand for in an ARIMA(p,d,q) process?
Degree of freedom
Order of autoregressive part
Order of moving average part
Order of integration
3
What is the purpose of the Jarque-Bera test?
Test for omitted variable bias
Test for normality
Test for stationarity
Test for autocorrelation
4
In a lin-log model relating electricity consumption to log income, β=15. What does this mean?
A 1-unit increase in income increases consumption by 0.15 units.
A 1% increase in income increases consumption by 15 units.
A 1% increase in income increases consumption by 0.15 units.
A 1-unit increase in income increases consumption by 15 units.
5
What is the null hypothesis in a t-test for statistical significance?
The coefficient equals zero.
The error term is zero.
The coefficient equals one.
The coefficient is significant.
6
A logit model is used to predict whether a student passes an exam. Which variable is the dependent variable?
Exam score (0-100)
Number of classes attended
Hours studied
Pass/fail (1=Pass, 0=Fail)
7
What is multicollinearity?
The error terms have unequal variances.
The dependent variable is highly correlated with the independent variables.
The relationship between y- and x-variables is not linear.
Two or more independent variables are highly correlated.
8
What does OLS stand for?
Ordinary Least Squares
Orthogonal Linear Smoothing
Optimal Logarithmic Scaling
Oh Lord, Statistics
9
The coefficient of determination R² can only be used for model comparison if ...
The x-variables are exactly the same.
The models have an intercept.
The number of the x-variables is exactly the same.
The y-variable of the models is exactly the same.
10
After running a logit model, you find the coefficient for "hours studied" is 0.5. What can you conclude?
The more hours studied, the higher the probability to pass the exam.
The more hours studied, the lower the probability to pass the exam.
Studying one extra hour increases the probability of passing by 0.5%.
The probability of passing is 50%.
11
Which of the following is NOT an assumption of the classical linear regression model?
The relationship between variables is linear.
There is no perfect multicollinearity.
The error term has a mean of zero.
The error terms are heteroscedastic.
12
A shock to a stationary process...
remains
vanishes
13
What problem(s) does heteroscedasticity cause?
Biased estimates
Inefficient estimates
Omitted variable bias
Non-linearity
14
Which test is commonly used to check for heteroscedasticity?
Hausman test
Durbin-Watson test
Dickey-Fuller test
Goldfeld-Quandt test

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