CFA® Level II Quantitative Methods - Heteroskedasticity: Why it is a problem and how to detect it

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Heteroskedasticity is a problem in statistics that occurs when the variance of a dependent variable (also known as the "error term") is not constant across all values of the predictor variables. This can lead to inaccurate results and invalid conclusions in statistical models.

There are several methods for detecting heteroskedasticity in a dataset, including visual inspections of residual plots and formal statistical tests such as the Breusch-Pagan test and the White test.

Correcting for heteroskedasticity can be done through the use of weighted least squares regression, where the weights are chosen to account for the varying variance in the error term. Alternatively, heteroskedasticity-consistent standard errors and covariance matrices can be used to correct for this issue in statistical inference.

In this video, we will discuss the impact of heteroskedasticity on statistical analysis and the various methods for detecting and correcting for this problem.