1. Ramsey Reset Test: a. Checks for misspecification of the regression model

b. Helps to detect multicollinearity c. Helps to detect non-stationarity d. A and B e. A and C 2. A test about general restrictions: a. Tests whether all parameters are equal to zero b. Is used to check whether the model has significant explanatory power c. Is done by conducting an F-test d. All the above 3. A variable of the regression equation is said to be significant statistics-wise if: __________________________________. 4. A variable of the regression equation is said to be insignificant statistics-wise if:

__________________________________.

5. R-squared: a. Takes any values between 0 and 1 inclusive b. The higher its value, the better the explanatory power of the model c. Equals the correlation coefficient squared

(note: R-squared could be high yet the model is statistically insignificant due to poor choice of regressors)

6. Adjusted R-squared: a. Increases with the number of regressors b. Increases only with the number of statistically significant explanatory variables c. Is always greater than R-squared d. A and C

In the regression of log(Y) on X, beta X captures the: __________________________________. 7. In the regression on the right, beta 2 captures: Y = 0 + 1 *log(X 1 ) + 2 *X 2 __________________________________. 8. To test whether the homoscedasticity assumption is satisfied you should: __________________________________. 9. The Breusch Pagan test: a. Checks whether the error u has the same variance given any value of the explanatory variables. b. Its null is in favor of heteroscedasticity c. Is more reliable in the case of large samples d. Is more reliable in the case of small samples e. A and D f. A and C 10. The R-squared of the regression, sometimes is called the coefficient of determination. True / False