genuinely independent observations) have nothing to hide. in the future newer solution might be implemented. Statas CRVE implementation is known as Rogers standard errors and is one of the first estimators. In that case, those terms will add to the variance of your estimator, but that is the appropriate thing in such a situation, since if your observations and errors are correlated, then you don't actually have as many independent observations as your simple sample size would indicate. Stata does not allow for two-way clustering, but the most important one for short-panels should be the cl(pid) option. $$\widehat \right] \neq 0 $ for $ j \neq k $. Cameron Trivedi (2005) (Microeconometrics: Methods and Applications) on p.834 give a very informative description of the variance estimator when using clustered errors for a linear model:
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