Understanding and dealing with Heteroskedasticity in regression analysis

Heteroskedasticity refers to the non-constant variance of a dependent variable across levels of an independent variable
In a linear regression model, heteroskedastic errors can lead to biased and inefficient coefficient estimates.

Testing for Heteroskedasticity

Common methods include the Breusch-Pagan test and the White test
These tests determine if the variance of errors is constant across levels of the independent variables
Dealing with Heteroskedasticity

Weighted least squares (WLS) involve weighting observations by the inverse of their variance
Heteroskedasticity-consistent standard errors (HCSE) adjust the standard errors of coefficients to account for non-constant error variance
Generalized Least Squares (GLS) can be used to estimate coefficients when the error term is heteroskedastic, by assuming a structure for the error variance and estimating its parameters
If the source of heteroskedasticity is identifiable, it is best to address it directly, such as by transforming variables or incorporating additional variables

Heteroskedasticity is a common problem in regression analysis
There are various methods for detecting and managing heteroskedasticity, including WLS, HCSE, and GLS
It’s important to address the underlying cause of heteroskedasticity, if possible, for accurate analysis.