The Post is about Online MCQs Econometrics. The MCQS Econometrics Quiz contains 20 multiple-choice questions on heteroscedasticity, multicollinearity, auxiliary regression, multiple regression, autocorrelation, and regression diagnostics. Let us start with the Online MCQs Econometrics quiz.

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

### Online MCQs Econometrics with 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 following actions does not make sense to struggle against multicollinearity?
- Autocorrelation may occur due to
- Which of the following can indicate negative autocorrelation?
- 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.