Corpus ID: 233481834

A Modified Randomization Test for the Level of Clustering

@inproceedings{Cai2021AMR,
  title={A Modified Randomization Test for the Level of Clustering},
  author={Yong Cai},
  year={2021}
}
Suppose a researcher observes individuals within a county within a state. Given concerns about correlation across individuals, at which level should they cluster their observations for inference? This paper proposes a modified randomization test as a robustness check for their chosen specification in a linear regression setting. Existing tests require either the number of states or number of counties to be large. Our method is designed for settings with few states and few counties. While the… Expand

Figures and Tables from this paper

Panel Data with Unknown Clusters
Clustered standard errors and approximated randomization tests are popular inference methods that allow for dependence within observations. However, they require researchers to know the clusterExpand

References

SHOWING 1-10 OF 20 REFERENCES
Testing for the appropriate level of clustering in linear regression models
The overwhelming majority of empirical research that uses cluster-robust inference assumes that the clustering structure is known, even though there are often several possible ways in which a datasetExpand
Randomization Tests Under an Approximate Symmetry Assumption
This paper develops a theory of randomization tests under an approximate symmetry assumption. Randomization tests provide a general means of constructing tests that control size in finite samplesExpand
Bootstrap-Based Improvements for Inference with Clustered Errors
  • The Review of Economics and Statistics,
  • 2008
Measuring Success in Education: The Role of Effort on the Test Itself
Tests measuring and comparing educational achievement are an important policy tool. We experimentally show that offering students extrinsic incentives to put forth effort on such achievement testsExpand
Inference with Few Heterogeneous Clusters
Abstract Suppose estimating a model on each of a small number of potentially heterogeneous clusters yields approximately independent, unbiased, and Gaussian parameter estimators. We make twoExpand
Bias reduction in standard errors for linear regression with multi-stage samples
A User's Guide to Approximate Randomization Tests with a Small Number of Clusters
This paper provides a user’s guide to the general theory of approximate randomization tests developed in Canay et al. (2017a) when specialized to linear regressions with clustered data. SuchExpand
Asymptotic Theory for Clustered Samples
We provide a complete asymptotic distribution theory for clustered data with a large number of groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, andExpand
THE WILD BOOTSTRAP WITH A “SMALL” NUMBER OF “LARGE” CLUSTERS
This paper studies the wild bootstrap–based test proposed in Cameron, Gelbach, and Miller (2008). Existing analyses of its properties require that number of clusters is “large.” In an asymptoticExpand
When Should You Adjust Standard Errors for Clustering?
TLDR
This paper argues that clustering is in essence a design problem, either a sampling design or an experimental design issue, and takes the view that this second perspective best fits the typical setting in economics where clustering adjustments are used. Expand
...
1
2
...