Question

Answer the following:

a. What do we mean by homoskedasticity?

b. What is the difference between homoskedasticity and heteroscedasticity?

c. What are the consequences of heteroscedasticity?

Answer 1

a)Homoscedasticity describes a situation in which the error term i.e. the “noise” or random disturbance in the relationship between the independent variables and the dependent variable is the same across all values of the independent variables.

b)homoscedasticity is (statistics) a property of a set of random variables where each variable has the same finite variance while heteroscedasticity is (statistics) the property of a series of random variables of not every variable having the same finite variance.

c)Consequences of Heteroscedasticity

The OLS estimators and regression predictions based on them remains unbiased and consistent. The OLS estimators are no longer the BLUE (Best Linear Unbiased Estimators) because they are no longer efficient, so the regression predictions will be inefficient too.