1, graphic test method: correlation diagram analysis; Residual diagram analysis.

2. Goldfield-Quant test.

3. White test.

4.Park test and Grics test.

Secondly, heteroscedasticity is relative to the same variance:

1, the so-called homovariance is to ensure that the regression parameter estimator has good statistical properties. An important assumption of the classical linear regression model is that the random error terms in the population regression function satisfy the homovariance, that is, they all have the same variance.

2. If this assumption is not satisfied, that is, the random error terms have different variances, the linear regression model is said to have heteroscedasticity.