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Why do we use heteroskedasticity robust standard errors?
Heteroskedasticity-consistent standard errors are used to allow the fitting of a model that does contain heteroskedastic residuals. 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.
Does Heteroskedasticity increase standard errors?
Heteroscedasticity does not cause ordinary least squares coefficient estimates to be biased, although it can cause ordinary least squares estimates of the variance (and, thus, standard errors) of the coefficients to be biased, possibly above or below the true of population variance.
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
How to deal with heteroskedasticity in a regression?
As I wrote above, by default, the type argument is equal to “HC3”. Another way of dealing with heteroskedasticity is to use the lmrob () function from the {robustbase} package. This package is quite interesting, and offers quite a lot of functions for robust linear, and nonlinear, regression models.
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.
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.