Heteroskedasticity of error term
Channel: Mikko Rönkkö
Date: 2019-07-24

In the context of regression analysis, heteroskedasticity of the error term occurs when the assumption of homoskedasticity (constant error variance) is violated. The video explains that heteroskedasticity is indicated by a pattern where the dispersion of error terms varies. The video uses the Deephouse article as an example. In this article, the authors identified that the residuals resembled a megaphone opening to the left. This variance in error terms leads to biased standard errors and inefficient estimates in ordinary least squares (OLS) regression.

The video further explores the impact of heteroskedasticity on regression estimates using two populations with different error term variances. It highlights how repeated samples from heteroskedastic data often result in more variable regression lines compared to homoskedastic data. To address these issues, the video suggests using weighted least squares as an alternative to OLS for more efficient estimates and applying heteroskedasticity robust standard errors to achieve consistent standard errors. The latter approach is recommended by some researchers for all cases, while others advise its use only when necessary, especially considering the potential bias in small samples. The video concludes with the importance of properly reporting heteroskedasticity in statistical analysis, as demonstrated in the Deephouse study, and the modern convenience of heteroskedasticity robust standard errors in leading statistical packages.

Slides: https://osf.io/d3xru