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When can you use robust standard errors?
Robust standard errors can be used when the assumption of uniformity of variance, also known as homoscedasticity, in a linear-regression model is violated. This situation, known as heteroscedasticity, implies that the variance of the outcome is not constant across observations.
What is the purpose of using 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 happens when you use robust standard errors in regression?
Note the changes in the standard errors and t-tests (but no change in the coefficients). In this particular example, using robust standard errors did not change any of the conclusions from the original OLS regression.
Can you do diagnostics with multinomial logistic regression?
Diagnostics and model fit: Unlike logistic regression where there are many statistics for performing model diagnostics, it is not as straightforward to do diagnostics with multinomial logistic regression models.
When to use robust option in Stata regression?
For such minor problems, the robust option may effectively deal with these concerns. With the robust option, the point estimates of the coefficients are exactly the same as in ordinary OLS, but the standard errors take into account issues concerning heterogeneity and lack of normality. Here is the same regression as above using the robust option.
Which is the best approach to robust regression?
We are going to look at three approaches to robust regression: 1) regression with robust standard errors including the cluster option, 2) robust regression using iteratively reweighted least squares, and 3) quantile regression, more specifically, median regression.