Models in which the error terms do not all have the same variance are said to exhibit:
A)heteroscedasticity.
B)autocorrelation.
C)specification bias.
D)multicollinearity.

Question

Quizplus · Accepted Answer

When the error terms do not have the same variance across all observations, it is called heteroscedasticity. This violates one of the assumptions of classical linear regression models and can result in biased coefficient estimates and incorrect hypothesis testing. Therefore, A is the correct answer. Autocorrelation refers to the correlation between error terms of a time series model, specification bias refers to errors in model specification, and multicollinearity refers to the presence of high correlation between independent variables in a regression model.

Models in Which the Error Terms Do Not All Have