Robust standard errors provide unbiased standard errors estimates under heteroscedasticity. There exists several statistical text books that provide a large and lengthy discussion on robust standard errors. This site provides a somewhat comprehensive summary on robust standard errors.

Coming back to your questions. Using robust standard errors is not without caveats. According to Woolridge (2009 edition, page 268) using robust standard errors, the t-statistics obtained only have distributions which are similar to the exact t-distributions if the sample size is large. If the sample size is small, the t-stats obtained using robust regression might have distributions that are not close to the t distribution. This could throw off inference. 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.

Robust standard errors provide unbiased standard errors estimates under heteroscedasticity. There exists several statistical text books that provide a large and lengthy discussion on robust standard errors. The following site provides a somewhat comprehensive summary on robust standard errors:

https://economictheoryblog.com/2016/08/07/robust-standard-errors/

Coming back to your questions. Using robust standard errors is not without caveats. According to Woolridge (2009 edition, page 268) using robust standard errors, the t-statistics obtained only have distributions which are similar to the exact t-distributions if the sample size is large. If the sample size is small, the t-stats obtained using robust regression might have distributions that are not close to the t distribution. This could throw off inference. 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.

Robust standard errors provide unbiased standard errors estimates under heteroscedasticity. There exists several statistical text books that provide a large and lengthy discussion on robust standard errors. The following site provides a somewhat comprehensive summary on robust standard errors:

https://economictheoryblog.com/2016/08/07/robust-standard-errors/

Coming back to your questions. Using robust standard errors is not without caveats. According to Woolridge (2009 edition, page 268) using robust standard errors, the t-statistics obtained only have distributions which are similar to the exact t-distributions if the sample size is large. If the sample size is small, the t-stats obtained using robust regression might have distributions that are not close to the t distribution. This could throw off inference. 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.

Robust standard errors provide unbiased standard errors estimates under heteroscedasticity. There exists several statistical text books that provide a large and lengthy discussion on robust standard errors. This site provides a somewhat comprehensive summary on robust standard errors.

Coming back to your questions. Using robust standard errors is not without caveats. According to Woolridge (2009 edition, page 268) using robust standard errors, the t-statistics obtained only have distributions which are similar to the exact t-distributions if the sample size is large. If the sample size is small, the t-stats obtained using robust regression might have distributions that are not close to the t distribution. This could throw off inference. 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.