Clusterrobuste

Clusterrobuste refers to statistical methods designed to produce valid inferences when data are organized into clusters, such as students within schools, patients within clinics, or repeated measurements within the same unit. The term is commonly used in econometrics and social sciences and is often found in phrases like cluster-robuste Standardfehler or cluster-robuste Kovarianzschätzer.

The central idea is that observations within the same cluster may be correlated, while observations across clusters are assumed independent. Traditional standard errors can be biased in the presence of such intra-cluster dependence, leading to overstated precision or incorrect test results. Clusterrobuste estimators adjust the estimated covariance matrix of coefficient estimates to account for within-cluster correlation, producing robust standard errors and more reliable hypothesis tests under clustering.

Implementation and usage are widespread in regression analysis and panel data models. Statistical software commonly offers cluster-robust options, allowing users to specify the clustering variable (for example by school, firm, or region). In practice, researchers rely on cluster-robust standard errors or robust covariance estimators to improve inference when clustering is present.

Limitations include the need for a sufficient number of clusters; with too few clusters, the standard errors may still be biased. Small-sample corrections and alternative resampling methods, such as cluster bootstrap, are sometimes recommended. Correct specification of the clustering structure is essential, as misspecification can bias results. Clusterrobuste methods are part of a broader family of robust inference techniques aimed at mitigating dependence and heterogeneity in empirical data.