Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust

TL;DR

The paper introduces the summclust package for Stata, providing cluster diagnostics and jackknife variance estimators to improve the reliability of inference in clustered linear regression models.

Contribution

It presents new cluster-level leverage, influence measures, and efficient jackknife variance estimators integrated into a single package for better inference.

Findings

Cluster diagnostics identify challenging inference scenarios.

Influence measures reveal data dependence on clusters.

Jackknife estimators are more conservative than standard methods.

Abstract

We introduce a new Stata package called summclust that summarizes the cluster structure of the dataset for linear regression models with clustered disturbances. The key unit of observation for such a model is the cluster. We therefore propose cluster-level measures of leverage, partial leverage, and influence and show how to compute them quickly in most cases. The measures of leverage and partial leverage can be used as diagnostic tools to identify datasets and regression designs in which cluster-robust inference is likely to be challenging. The measures of influence can provide valuable information about how the results depend on the data in the various clusters. We also show how to calculate two jackknife variance matrix estimators efficiently as a byproduct of our other computations. These estimators, which are already available in Stata, are generally more conservative than conventional variance matrix estimators. The summclust package computes all the quantities that we discuss.