Inference with Correlated Clusters

44 Pages Posted: 11 Jun 2016 Last revised: 2 Jun 2017

Date Written: May 18, 2017

Abstract

This paper introduces a method which permits valid inference given a finite number of heterogeneous, correlated clusters. Many inference methods assume clusters are asymptotically independent or model dependence across clusters as a function of a distance metric. With panel data, these restrictions are unnecessary. This paper relies on a test statistic using the mean of the cluster-specific scores normalized by the variance and simulating the distribution of this statistic. To account for cross-cluster dependence, the relationship between each cluster is estimated, permitting the independent component of each cluster to be isolated. The method is simple to implement, can be employed for linear and nonlinear estimators, places no restrictions on the strength of the correlations across clusters, and does not require prior knowledge of which clusters are correlated or even the existence of independent clusters. In simulations, the procedure rejects at the appropriate rate even in the presence of highly-correlated clusters.

Keywords: Finite Inference, Correlated Clusters, Fixed Effects, Panel Data, Hypothesis Testing, Score Statistic

JEL Classification: C12, C21, C23, C33

Suggested Citation

Powell, David, Inference with Correlated Clusters (May 18, 2017). RAND Working Paper Series WR- 1137-1. Available at SSRN: https://ssrn.com/abstract=2793555 or http://dx.doi.org/10.2139/ssrn.2793555

David Powell (Contact Author)

RAND Corporation ( email )

1776 Main Street
P.O. Box 2138
Santa Monica, CA 90407-2138
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
45
Abstract Views
388
PlumX Metrics