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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
Is the calculation of robust standard errors a problem?
Fortunately, the calculation of robust standard errors can help to mitigate this problem.
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 are robust standard errors reported in EViews?
When robust standard errors are employed, the numerical equivalence between the two breaks down, so EViews reports both the non-robust conventional residual and the robust Wald F-statistics. EViews reports the robust F-statistic as the Wald F-statisticin equation output, and the corresponding p-value as Prob(Wald F-statistic).
How is heteroscedasticity used in a linear model?
The empirical data includes heteroscedasticity, but I am not interested in transforming it away, but rather using a linear model with an error term to reproduce simulations of the empirical data. For example, let’s say I have some an empirical dataset and a model:
How are robust standard errors computed in Stata?
With Stata, robust standard errors can usually be computed via the addition of two parameters, robust and cluster. The robust option relaxes the assumption that the errors are identically distributed, while cluster relaxes the assumption that the error terms are independent of each other.
Which is the best Test to test for heteroskedasticity?
Let’s test for heteroskedasticity using the Breusch-Pagan test that you can find in the {lmtest} package: This test shows that we can reject the null that the variance of the residuals is constant, thus heteroskedacity is present.
What happens when you use robust standard errors?
Notice that when we used robust standard errors, the standard errors for each of the coefficient estimates increased. Note: In most cases, robust standard errors will be larger than the normal standard errors, but in rare cases it is possible for the robust standard errors to actually be smaller.