Ordinary OLS standard errors or robust standard errors?

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           • You may wonder when you should use robust standard errors and when you should
use ordinary OLS standard errors.
          • The advantage of OLS standard errors is that t-tests using them are exact tests, as long
as the assumptions of the classical linear regression model are satisfied.
          • So OLS standard errors are better if you believe these assumptions are reasonable.
          • Robust standard errors are good if you have a large data set and you do not want to
rely on assumptions regarding the form of heteroskedasticity present.
          • On the other hand, if you have a small data set and you know heteroskedasticity is a
problem, your best option is to use WLS and transform the model to one where the
CLM assumptions hold.
           In a word,small data set ordinary OLS,big dada set Robust.

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关键词：Standard Ordinary Errors robust Error ordinary standard errors

For example, your regression model is y = a + bx1 + bx2.
First, run this regression model using the OLS by writing: regress y x1 x2
Second, perform the diagnostic check for your residuals. If you have detected heteroscedasticity, you should re-run your model by using the robust standard errors: regress y x1 x2, r

Thank  you very much! After read your comment I know smomething about  the  roubst。

请问Heteroskedasticity-robust standard errors中文该如何翻译呢？