Cluster-robust jackknife and bootstrap inference for binary response models - James G. MacKinnon; Morten Ørregaard Nielsen; Matthew Webb (Queen's University, Department of Economics, 2024)
We study cluster-robust inference for binary response models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. Conceptually the simplest of these, but also the most computationally demanding, involves jackknifing at the cluster level. We also propose a linearized version of the cluster-jackknife variance matrix estimator as well as linearized versions of the wild cluster bootstrap. The linearizations are based on empirical scores and are computationally efficient. Throughout we use the logit model as a leading example. We also discuss a new Stata software package called logitjack which implements these procedures. Simulation results strongly favor the new methods, and two empirical examples suggest that it can be important to use them in practice.
Keywords: C12; C15; C21; C23; 330; logit model; logistic regression; clustered data; grouped data; cluster-robust variance estimator; CRVE; cluster jackknife; robust inference; wild cluster boot-strap; linearization