What are the test for heteroscedasticity?
Heteroscedasticity refers to the unequal distribution of variances (or differences) in a dataset, usually within residuals in regression analysis. Several tests are available to detect heteroscedasticity, including:
1. The Breusch-Pagan test: This test is based on the assumption that residuals can be linearly related to explanatory variables. It examines if the estimated variances are significantly different from each other, which indicates heteroscedasticity.
2. The White test: A more general approach than the Breusch-Pagan test, the White test allows for nonlinear correlations between residuals and explanatory variables. This test is particularly useful when you have a large number of explanatory variables and complicated distributions.
3. The Goldfeld-Quandt test: This test involves splitting the dataset into smaller parts and running separate regression analyses on each partition. If the variances of the two samples are significantly different, heteroscedasticity is present.
4. The Bartlett test: An extension of the Levene test particularly useful for comparing multiple groups to test for heteroscedasticity. The Bartlett test calculates the ratio of the largest sample variance to the smallest sample variance and tests if the difference is statistically significant.
5. The Levene test: A less extreme version of the Bartlett test, it tests if the difference in variances across groups is significant based on their means or medians.
6. The Glejser test: This test is based on regressing the absolute residuals on explanatory variables. A significant relationship between the absolute residuals and explanatory variables indicates heteroscedasticity.
These tests can help identify heteroscedasticity in datasets, enabling researchers to address and account for this issue in their data analysis process.