Heteroskedasticity
Candlefocus EditorAt a basic level, heteroskedasticity means that the average errors of the model in estimating y (dependent variable) will increase or decrease. It presents a potential problem as the model results may be over or under estimated and can lead to incorrect or inaccurate conclusions.
There are two basic tests that can be used to detect the presence of heteroskedasticity. The Breusch-Pagan test is used to measure the presence of heteroskedasticity in residuals, while the Cook-Weisberg test measures the relationship between the dependent variable and the variables used to determine the model’s estimated parameters.
Heteroskedasticity can be addressed in a variety of ways. For example, a researcher can use transformation methods, such as a Box-Cox transformation, to address this problem. Another approach is to use generalized least squares (GLS), which is more robust to heteroskedasticity than the standard least-squares approach. GLS can also be applied to models where the non-constant variance is unknown.
Finally, if the nature of the heteroskedasticity can be identified, then other appropriate techniques can be used to address the problem. For instance, if the heteroskedasticity is caused by the presence of outliers in the dataset, then techniques such as robust regression or shrinkage methods can be used to address the issue.
Heteroskedasticity is an important issue to address when undertaking econometric, financial, or statistical analysis as it has the potential to invalidate the results of a model. As such, it is important to use the appropriate tests to detect the presence of heteroskedasticity, and to choose an appropriate technique for addressing the issue. Through these steps, researchers can ensure that their models are valid and that their results are not affected by non-constant variance in the data.