Heteroskedasticity-consistent standard errors

When we try to estimate a relationship between two variables (like height and weight),we can use statistical techniques to make sure our estimate is correct and accurate. But sometimes, our data does not follow certain rules that make our estimate less accurate.

One rule that our data should follow is called homoskedasticity. This means that the variability of the error term (how much the estimate differs from the actual value) should be the same for all levels of the independent variable (like age).

However, sometimes our data does not follow this rule and the variability of the errors is different for different levels of the independent variable.This is called heteroskedasticity.

Heteroskedasticity can cause problems when we estimate our relationship between the two variables because it makes our standard errors less reliable. Standard errors are important because they tell us how accurate our estimates are and how certain we are about them.

Therefore, we use heteroskedasticity-consistent standard errors to help account for the problem of heteroskedasticity. These standard errors take into account that the variability of the error term is different for different levels of the independent variable. This makes our estimate more accurate because it allows us to have a better understanding of how much our estimates are likely to vary due to the heteroskedasticity in our data.

In simpler terms, heteroskedasticity-consistent standard errors are like a special tool that helps us get a more accurate estimate of the relationship between two variables when our data doesn't follow certain rules.