Should you always use robust standard errors?
Thus, it is safe to use the robust standard errors (especially when you have a large sample size.) Even if there is no heteroskedasticity, the robust standard errors will become just conventional OLS standard errors. Thus, the robust standard errors are appropriate even under homoskedasticity.
What do robust standard errors do?
“Robust” standard errors is a technique to obtain unbiased standard errors of OLS coefficients under heteroscedasticity. Remember, the presence of heteroscedasticity violates the Gauss Markov assumptions that are necessary to render OLS the best linear unbiased estimator (BLUE).
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.
When to use’robust’or’areg’fixed effects command?
However, when using ‘xtreg’ or ‘areg’ commands, options ‘robust’ and ‘cluster (clusterid)’ produce the same standard errors, that’s why I thought I could use the first one just for convenience. Is that right? I don’t know if you were aware of a similar previous discussion in this forum. I hope this throws some more light to your question.
When to use clustered-robust standard Erros in panel anlaysis?
What is important is that both White and clustered SEs are asymptotic results. For valid inference using White SEs, you need the number of individuals to go to infinity. When using clustered SEs, you need the number of clusters to go to infinity.
Can You Use robust in the HT format?
Remember, you cannot use “robust” with the fisher format or the ht format. You can use it only with the breitung and hadri formats. However, “robust” has a different meaning in these two cases. With respect to hadri, it gives a test statistic that is robust to heteroscedasticity across panels.