There are several approaches to dealing with heteroscedasticity. If the error variance at different times is known, weighted regression is a good method. If, as is ...

Financial word of the day: Heteroscedasticity is one of the most important but least understood terms in statistics, data science, and economic research. It describes a situation where the variability ...

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Testing for heteroscedasticity is a common diagnostic practice in regression analysis. Depending upon the outcome of the test, the model is either estimated by OLS or WLS. The results of a Monte Carlo ...

One of the key assumptions of the ordinary regression model is that the errors have the same variance throughout the sample. This is also called the homoscedasticity ...

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Doss, Charles R., and Edward McFowland III. "Nonparametric Subset Scanning for Detection of Heteroscedasticity." Journal of Computational and Graphical Statistics 31, no. 3 (2022): 813–823.

Abstract This paper proposes a two-component realized exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model – an extension of the realized EGARCH model – for the joint ...