Description

Unit Overview 

Unit 5 examines what happens if one of the assumptions of the classical linear regression model is not met, when the disturbance terms are heteroscedastic – that is, when the variance of the disturbance terms is not constant. 

Learning outcomes 

After studying Unit 5, the associated readings, and completing the exercises, you will be able to: 

• explain what is meant by heteroscedasticity 

• discuss the consequences of heteroscedasticity for OLS estimators and inference based on them 

• use graphs to identify heteroscedasticity 

• use auxiliary regressions to test for heteroscedasticity 

• use weighted least squares to estimate models where heteroscedasticity is present 

• transform equations to eliminate heteroscedasticity