Heteroskedasticity (Value of Error Term Are Not Constant)
-Violates classical assumption 5, that errors have constant variance
(Var( i ) = σ2, ∀i )
-Not always realistic: if we put basketball players and mice into the
same dataset, most likely errors for basketball players would have
larger variance than errors for mice (if measured in the same units)
-Most likely to take place in cross-sectional models
-OLS estimates of ˆβ remain unbiased but have similar problems to
A classic example of heteroscedasticity is that of income versus expenditure on meals. As one's income increases, the variability of food consumption will increase. A poorer person will spend a rather constant amount by always eating less expensive food; a wealthier person may occasionally buy inexpensive food and at other times eat expensive meals. Those with higher incomes display a greater variability of food consumption.
There are several methods to test for the presence of heteroscedasticity:
1) Park test
2) White test
3) Breusch-Pagan test
Remedies for heteroskedasticity
1) Weighted least squares
2) Heteroskedasticity-corrected standard errors
3) Redeﬁning the variables
To Detect Heteroscedasticity:
Plot values of residual againts value of independent variables..
Increase sample size data @ by droping one of the highly correlated variables..