When working with panel data we should generally use clustered standard errors?
Clustered standard errors are for accounting for situations where observations WITHIN each group are not i.i.d. (independently and identically distributed). A classic example is if you have many observations for a panel of firms across time.
Why do we need clustered standard errors?
The authors argue that there are two reasons for clustering standard errors: a sampling design reason, which arises because you have sampled data from a population using clustered sampling, and want to say something about the broader population; and an experimental design reason, where the assignment mechanism for some …
When to use clustered-robust standard Erros in panel anlaysis?
What is important is that both White and clustered SEs are asymptotic results. For valid inference using White SEs, you need the number of individuals to go to infinity. When using clustered SEs, you need the number of clusters to go to infinity.
Why are there clusters of errors in Stata?
If students were randomly sampled to model test scores as a function of classes taken (measured at the individual level, not the school level), but classes taken and their effects on test scores are correlated within school, this may also induce clustering of errors at the higher level (school in the example).
Is it better to use clustered or robust standard errors?
I have been reading Abadie et. al (2019) and they tell that, in case there is no heterogeneity on the treatment, it does not make a difference if you use robust standard errors or clustered.
When is a cluster too big for CRSE?
Rogers (1993) argues that “if no cluster is larger than 5 percent or so of the total sample, the standard errors will not be too far off because each term will be off by less than 1 in 400.” This implies that CRSE’s with 20 equal-sized clusters would suffer from a very small bias.