MCQs Econometrics 1

This Post is about MCQs Econometrics, which covers the topics of Regression analysis, correlation, dummy variable, multicollinearity, heteroscedasticity, autocorrelation, and many other topics. Let’s start with MCQs Econometric test

MCQs about Multicollinearity, Dummy Variable, Selection of Variables, Error in Variables, Autocorrelation, Time Series, Heteroscedasticity, Simultaneous Equations, and Regression analysis

1. What technique models a categorical variable based on one or more independent variables?

 
 
 
 

2. In a linear regression model, what is the area surrounding the regression line that describes the uncertainty around the predicted outcome at every value of X?

 
 
 

3. Which one is not the rule of thumb?

 
 
 
 

4. The AR(1) process is stationary if

 
 
 
 

5. Choose a true statement about the Durbin-Watson test

 
 
 
 

6. What type of regression models a categorical variable based on one or more independent variables?

 
 
 
 

7. For the presence and absence of first-order autocorrelation valid tests are

 
 
 
 

8. Autocorrelation may occur due to

 
 
 
 

9. If a Durbin Watson statistic takes a value close to zero what will be the value of the first-order autocorrelation coefficient?

 
 
 
 

10. Heteroscedasticity can be detected by plotting the estimated $\hat{u}_i^2$ against

 
 
 
 

11. Which of the following statements is true about autocorrelation?

 
 
 
 

12. Negative autocorrelation can be indicated by which of the following?

 
 
 
 

13. The _____ states that no two independent variables ($X_i$ and $X_j$) can be highly correlated with each other.

 
 
 
 

14. The value of Durbin Watson $d$ lies between

 
 
 
 

15. Which of the actions does not make sense to take in order to struggle against multicollinearity?

 
 
 
 

16. What is a nonlinear function that connects or links a dependent variable to the independent variables mathematically?

 
 
 
 

17. Which one assumption is not related to error in explanatory variables?

 
 
 
 

18. In a regression model with three explanatory variables, there will be _______ auxiliary regressions

 
 
 
 

19. Heteroscedasticity is more common in

 
 
 
 

20. When measurement errors are present in the explanatory variable(s) they make

 
 
 
 


An application of different statistical methods applied to the economic data used to find empirical relationships between economic data is called Econometrics. In other words, Econometrics is “the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference”.

Online MCQs Econometrics Answers

  • In a regression model with three explanatory variables, there will be auxiliary regressions
  • The AR(1) process is stationary if
  • The value of Durbin Watson $d$ lies between
  • Which of the actions does not make sense to take in order to struggle against multicollinearity?
  • Autocorrelation may occur due to
  • Negative autocorrelation can be indicated by which of the following?
  • Which one is not the rule of thumb?
  • Heteroscedasticity can be detected by plotting the estimated $\hat{u}_i^2$ against
  • If a Durbin Watson statistic takes a value close to zero what will be the value of the first-order autocorrelation coefficient?
  • Which of the following statements is true about autocorrelation?
  • When measurement errors are present in the explanatory variable(s) they make
  • Heteroscedasticity is more common in
  • Which one assumption is not related to error in explanatory variables?
  • For the presence and absence of first-order autocorrelation valid tests are
  • Choose a true statement about the Durbin-Watson test
  • What technique models a categorical variable based on one or more independent variables?
  • What is a nonlinear function that connects or links a dependent variable to the independent variables mathematically?
  • What type of regression models a categorical variable based on one or more independent variables?
  • In a linear regression model, what is the area surrounding the regression line that describes the uncertainty around the predicted outcome at every value of $X$?
  • The states that no two independent variables ($X_i$ and $X_j$) can be highly correlated with each other.
MCQs Econometrics

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