Abstract
The article studies the effectiveness of various statistical tests for heteroscedasticity in a model. A research design and a principle for building synthetic data with various types of heteroscedasticity are described. The fi ndings of an analysis are given. The most effective tests for detecting homo- and heteroscedasticity are determined. A classifi cation trees mechanism is applied to identify the most effective tests according to the sampling properties, and such pattern is demonstrated. In applied studies, there is a need to carry out further research aimed at detecting the most suitable statistical test based on the given data properties. In addition, it is concluded that each considered test fails for different types of heteroscedasticity. Thus, it is necessary to conduct further theoretical studies in the fi eld as well as design new approaches for detecting various types of heteroscedasticity.
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