Wild Bootstrap Inference for Wildly Different Cluster Sizes

Wild Bootstrap Inference for Wildly Di erent Cluster Sizes ∗

The cluster robust variance estimator (CRVE) relies on the number of clusters being large. The precise meaning of `large' is ambiguous, but a shorthand `rule of 42' has emerged. We show that this rule is invalid when clusters are not equal-sized. Monte Carlo evidence suggests that rejection frequencies can be much higher when a dataset has 50 clusters proportional to the populations of the US s...

Likelihood inference for exchangeable binary data with varying cluster sizes.

This article investigates maximum likelihood estimation with saturated and unsaturated models for correlated exchangeable binary data, when a sample of independent clusters of varying sizes is available. We discuss various parameterizations of these models, and propose using the EM algorithm to obtain maximum likelihood estimates. The methodology is illustrated by applications to a study of fam...

A Score Based Approach to Wild Bootstrap Inference

We propose a generalization of the wild bootstrap of Wu (1986) and Liu (1988) based upon perturbing the scores of M-estimators. This “score bootstrap” procedure avoids recomputing the estimator in each bootstrap iteration, making it substantially less costly to compute than the conventional nonparametric bootstrap, particularly in complex nonlinear models. Despite this computational advantage, ...

Cluster-robust Bootstrap Inference in Quantile Regression Models

In this paper I develop a wild bootstrap procedure for cluster-robust inference in linear quantile regression models. I show that the bootstrap leads to asymptotically valid inference on the entire quantile regression process in a setting with a large number of small, heterogeneous clusters and provides consistent estimates of the asymptotic covariance function of that process. The proposed boo...

The Wild Bootstrap ,