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In This Chapter
-
Robust Inference with Clustered Data
- Introduction
- Clustering and Its Consequences
- Cluster-Robust Inference for OLS
- Cluster-Robust Inference
- Specifying the Clusters
- Cluster-Specific Fixed Effects
- Many Observations per Cluster
- Survey Design with Clustering and Stratification
- Inference with Few Clusters
- Finite-Sample Adjusted Standard Errors
- Finite-Sample Wald Tests
- T Distribution for Inference
- Cluster Bootstrap with Asymptotic Refinement
- Few Treated Groups
- Multi-Way Clustering
- Feasible GLS
- FGLS and Cluster-Robust Inference
- Efficiency Gains of Feasible GLS
- Random Effects Model
- Hierarchical Linear Models
- Serially Correlated Errors Models for Panel Data
- Nonlinear and Instrumental Variables Estimators
- Empirical Example
- Conclusion
- References
Abstract
In this survey we consider regression analysis when observations are grouped in clusters, with independence across clusters but correlation within clusters. We consider this in settings where estimators retain their consistency, but statistical inference based on the usual cross-section assumption of independent observations is no longer appropriate.
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