Suppose you are trying to decide whether an independent variable should appear in level (nonnested Model 1) or logarithmic (nonnested Model 2) form. Since these are nonnested models, we cannot use a standard Ftest to test for joint significance. Model 1 y = Bo++ B212+u Model 2 y= Bo + Blog(1) + Balog(+2)+u True or False: One way to test Model 1 against Model 2, to determine the form in which the independent variable should appear, is by estimating Model 1 by OLS and using the fitted values, y, from this as another regressor in the original Model 2. From here, the Davidson-Mackinnon test is obtained from the statistic on the fitted values, y, in the auxiliary equation: y = Bo + Bilog(+1) + Bzlog(+2) +019 + error. (Hint: Assume that Model 1 holds with E(031, #2) = 0.) True False Which of the following describe(s) potential issues with nonnested testing? Check all that apply. The nonnested testing does not apply to cases when the models have different dependent variables. Rejecting Model 1 does not mean that Model 2 is the correct model. Both models could be rejected, or neither model could be rejected. Rejecting Model 2 implies that Model 1 is the correct model. Suppose you are trying to decide whether an independent variable should appear in level (nonnested Model 1) or logarithmic (nonnested Model 2) form. Since these are nonnested models, we cannot use a standard Ftest to test for joint significance. Model 1 y = Bo++ B212+u Model 2 y= Bo + Blog(1) + Balog(+2)+u True or False: One way to test Model 1 against Model 2, to determine the form in which the independent variable should appear, is by estimating Model 1 by OLS and using the fitted values, y, from this as another regressor in the original Model 2. From here, the Davidson-Mackinnon test is obtained from the statistic on the fitted values, y, in the auxiliary equation: y = Bo + Bilog(+1) + Bzlog(+2) +019 + error. (Hint: Assume that Model 1 holds with E(031, #2) = 0.) True False Which of the following describe(s) potential issues with nonnested testing? Check all that apply. The nonnested testing does not apply to cases when the models have different dependent variables. Rejecting Model 1 does not mean that Model 2 is the correct model. Both models could be rejected, or neither model could be rejected. Rejecting Model 2 implies that Model 1 is the correct model