Misspecification of functional form of the regression function
A)is overcome by adding the squares of all explanatory variables.
B)is more serious in the case of homoskedasticity-only standard error.
C)results in a type of omitted variable bias.
D)requires alternative estimation methods such as maximum likelihood.

Question

Quizplus · Accepted Answer

Misspecification of functional form of the regression function can result in a type of omitted variable bias. This occurs when there are other explanatory variables that should be included in the model but are not, leading to biased estimated coefficients for the included variables. Adding the squares of all explanatory variables (choice A) is one way to address nonlinearity in the relationship between the dependent variable and an explanatory variable, but it does not necessarily address misspecification of the functional form. Homoskedasticity-only standard error (choice B) refers to assuming that the error terms have the same variance for all observations, which is a separate issue from functional form misspecification. While alternative estimation methods such as maximum likelihood (choice D) may be used to address misspecification of the functional form, they are not necessarily required.

Misspecification of Functional Form of the Regression Function