Table of Contents
When should you adjust standard errors for clustering?
The conventional framework for clustering (e.g., (Cameron and Miller, 2015; MacKinnon et al., 2021)) suggests that if the clustering adjustment matters, in the sense that the cluster standard errors are substantially larger than the robust standard errors, one should use the cluster standard errors.
How does clustering affect standard errors?
Analogous to how Huber-White standard errors are consistent in the presence of heteroscedasticity and Newey–West standard errors are consistent in the presence of accurately-modeled autocorrelation, clustered (or “Liang-Zeger”) standard errors are consistent in the presence of cluster-based sampling or treatment …
Why is it important to cluster standard errors?
Clustered standard errors are used in regression models when some observations in a dataset are naturally “clustered” together or related in some way. To understand when to use clustered standard errors, it helps to take a step back and understand the goal of regression analysis.
Do you need to cluster standard errors with fixed effects?
If there’s no heterogeneity in the treatment effects and assignments have not been clustered, you don’t have to use clustered standard errors. If you’re using fixed effects, this requirement is looser. If there’s no heterogeneity in the treatment effects, you don’t have to use clustered standard errors.
Does clustering increase standard errors?
According to Cameron and Miller, this clustering will lead to: Standard errors that are smaller than regular OLS standard errors. Narrow confidence intervals.
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.
When Should You Adjust Standard Errors for Clustering? In empirical work in economics it is common to report standard errors that account for clustering of units. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated.
What motivates clustering adjustments?
Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated.
How do I fit a regression model with clustered standard errors?
For example, in Stata you can use the cluster (variable name) command to tell Stata to use clustered standard errors when fitting a regression model. In practice, you can use the following syntax to fit a regression model in Stata with clustered standard errors: regress x y, cluster (variable_name)
What is the justification for clustering?
Another common and logically distinct justification for clustering arises when a full population cannot be randomly sampled, and so instead clusters are sampled and then units are randomized within cluster.