Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust
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.
Code (1)
Tasks
DiagnosticregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Jackknife inference with two-way clustering
For linear regression models with cross-section or panel data, it is natural to assume that the disturbances are clustered in two dimensions. However, the finite-sample properties of two-way cluster-robust tests and conf…
ClusteringFast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference
We provide computationally attractive methods to obtain jackknife-based cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. We also propose several new variants of t…
regressionDiscriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Deep learning models achieve high predictive accuracy across a broad spectrum of tasks, but rigorously quantifying their predictive uncertainty remains challenging. Usable estimates of predictive uncertainty should (1) c…
Uncertainty QuantificationCluster-robust jackknife and bootstrap inference for logistic regression models
We study cluster-robust inference for logistic regression (logit) models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. C…
regressionConformalized Polynomial Chaos Expansion for Uncertainty-aware Surrogate Modeling
This work introduces a method to equip data-driven polynomial chaos expansion surrogate models with intervals that quantify the predictive uncertainty of the surrogate. To that end, jackknife-based conformal prediction i…