Heteroskedastisitetrobuste

Heteroskedastisitetrobuste refers to a statistical method or estimator designed to be valid even when the assumption of homoskedasticity is violated. In econometrics and statistics, homoskedasticity means that the variance of the error term in a regression model is constant across all levels of the independent variables. Heteroskedasticity, on the other hand, occurs when this variance is not constant. This can lead to inefficient and biased standard errors in standard regression analyses.

Heteroskedastisitetrobuste standard errors, often referred to as robust standard errors or White standard errors, are a common approach to address heteroskedasticity. They provide a way to calculate standard errors that are consistent even in the presence of heteroskedasticity. This means that hypothesis tests and confidence intervals based on these robust standard errors will have the correct size and coverage probabilities.

The concept extends beyond just standard errors. Heteroskedastisitetrobuste estimators are techniques that aim to produce reliable parameter estimates and inferences even when the error variance is unequal. While OLS (Ordinary Least Squares) estimators are still consistent under heteroskedasticity, their standard errors are not. Robust methods ensure that the inferential statistics are sound. The use of heteroskedastisitetrobuste methods is crucial in many real-world applications where the assumption of constant error variance is often unrealistic.