What do robust standard errors tell you?
“Robust” standard errors is a technique to obtain unbiased standard errors of OLS coefficients under heteroscedasticity. “Robust” standard errors have many labels that essentially refer all the same thing. Namely, standard errors that are computed with the sandwich estimator of variance.
What does standard error Tell us in logistic regression?
The standard error of the regression (S), also known as the standard error of the estimate, represents the average distance that the observed values fall from the regression line. Conveniently, it tells you how wrong the regression model is on average using the units of the response variable.
Do robust standard errors fix Heteroskedasticity?
In practice, we usually do not know the structure of heteroskedasticity. 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.
How does Heteroskedasticity affect 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 do you interpret standard error?
The standard error tells you how accurate the mean of any given sample from that population is likely to be compared to the true population mean. When the standard error increases, i.e. the means are more spread out, it becomes more likely that any given mean is an inaccurate representation of the true population mean.
What does a high standard error mean?
A high standard error shows that sample means are widely spread around the population mean—your sample may not closely represent your population. A low standard error shows that sample means are closely distributed around the population mean—your sample is representative of your population.
Are robust standard errors efficient?
Furthermore, in case of homoscedasticity, robust standard errors are still unbiased. However, they are not efficient. That is, conventional standard errors are more precise than robust standard errors. Finally, using robust standard errors is common practice in many academic fields.
Can robust standard errors be smaller?
The lesson we can take a away from this is that robust standard errors are no panacea. They can be smaller than OLS standard errors for two reasons: the small sample bias we have discussed, and the higher sampling variance of these standard errors.
Why would a researcher use robust standard errors?
Robust standard errors are useful in social sciences where the structure of variation is unknown, but usually shunned in physical sciences where the amount of variation is the same for each observation. Robust standard errors are generally larger than non-robust standard errors, but are sometimes smaller.
What does standard error mean in logit model?
For continuous-continuous interactions (and perhaps continuous-dummy as well), that is generally not the case in non-linear models like the logit. The standard error indicates the uncertainty of the coefficients. One simple way to get a feeling for the uncertainty is to extract random subset of your data and compare the coefficients for each.
What is the use of robust standard errors?
The “robust” standard errors are being reported to cover the possibility that the model’s errors may be heteroskedastic. But if that’s the case, the parameter estimates are inconsistent. What use is a consistent standard error when the point estimate is inconsistent?
Why are sandwich standard errors used in logit regression?
Because the basic assumption for the sandwich standard errors to work is that the model equation (or more precisely the corresponding score function) is correctly specified while the rest of the model may be misspecified.
Can a robust Huber-White estimator be used in logistic regression?
You can always get Huber-White (a.k.a robust) estimators of the standard errors even in non-linear models like the logistic regression. However, if you believe your errors do not satisfy the standard assumptions of the model, then you should not be running that model as this might lead to biased parameter estimates.