What is Heteroskedasticity robust?
Standard errors based on this procedure are called (heteroskedasticity) robust standard errors or White-Huber standard errors. Or it is also known as the sandwich estimator of variance (because of how the calculation formula looks like). Thus, the robust standard errors are appropriate even under homoskedasticity.
How do you calculate Heteroskedasticity?
To check for heteroscedasticity, you need to assess the residuals by fitted value plots specifically. Typically, the telltale pattern for heteroscedasticity is that as the fitted values increases, the variance of the residuals also increases.
How are Heteroskedasticity and robust estimators related?
Standard errors based on this procedure are called (heteroskedasticity) robust standard errors or White-Huber standard errors. Or it is also known as the sandwich estimator of variance (because of how the calculation formula looks like). This procedure is reliable but entirely empirical. We do not impose any assumptions on the
Why are standard errors not reliable in heteroskedasticity?
Since standard model testing methods rely on the assumption that there is no correlation between the independent variables and the variance of the dependent variable, the usual standard errors are not very reliable in the presence of heteroskedasticity. Fortunately, the calculation of robust standard errors can help to mitigate this problem.
When was heteroscedasticity introduced in GARCH estimation?
The first such approach was proposed by Huber (1967), and further improved procedures have been produced since for cross-sectional data, time-series data and GARCH estimation . Heteroscedasticity-consistent standard errors that differ from classical standard errors are an indicator of model misspecification.
How is heteroscedasticity used in econometrics and statistics?
The topic of heteroscedasticity-consistent ( HC) standard errors arises in statistics and econometrics in the context of linear regression and time series analysis.