Should I cluster my standard errors?
Therefore, one should cluster at the highest level of aggregation possible, subject to finite sample issues: if clustering matters, it should be done, and if it does not matter, clustering the standard errors does no harm, at least in large samples.
Should I use robust or clustered standard errors?
Robust standard errors are generally larger than non-robust standard errors, but are sometimes smaller. Clustered standard errors are a special kind of robust standard errors that account for heteroskedasticity across “clusters” of observations (such as states, schools, or individuals).
What does it mean for standard errors to be clustered?
What are Clustered Standard Errors? Clustered Standard Errors(CSEs) happen when some observations in a data set are related to each other. This correlation occurs when an individual trait, like ability or socioeconomic background, is identical or similar for groups of observations within clusters.
Does clustering increase or decrease standard errors?
Robust clustered standard errors can change your standard errors in both directions. That is, clustered standard errors can be larger or smaller than conventional standard errors. The direction in which standard errors will change depends on the sign of the intra-class correlation.
Why would 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.
Why use robust standard errors Stata?
One way to account for this problem is to use robust standard errors, which are more “robust” to the problem of heteroscedasticity and tend to provide a more accurate measure of the true standard error of a regression coefficient.
Why are clustered standard errors larger?
In such DiD examples with panel data, the cluster-robust standard errors can be much larger than the default because both the regressor of interest and the errors are highly correlated within cluster. Note also that this complication can exist even with the inclusion of fixed effects (see Section III).
What is a fixed effect variable?
Fixed effects are variables that are constant across individuals; these variables, like age, sex, or ethnicity, don’t change or change at a constant rate over time. They have fixed effects; in other words, any change they cause to an individual is the same.
Why standard error is high in cluster sampling?
In fact if secondary units within a cluster tend to be more similar to each other than to units in other clusters, then the true standard error of your estimates will be much higher than those obtained from simple random sampling.
Why are robust standard errors smaller?
Comment: On p. 307, you write that robust standard errors “can be smaller than conventional standard errors for two reasons: the small sample bias we have discussed and their higher sampling variance.” A third reason is that heteroskedasticity can make the conventional s.e. upward-biased.