A0278
Title: Wild bootstrap for instrumental variables regressions with weak and few clusters
Authors: Wenjie Wang - Nanyang Technological University (Singapore) [presenting]
Abstract: The wild bootstrap inference is studied for instrumental variable regressions in the framework of a small number of large clusters in which the number of clusters is viewed as fixed and the number of observations for each cluster diverges to infinity. We first show that the wild bootstrap Wald test, with or without using the cluster-robust covariance matrix, controls size asymptotically up to a small error and has power against local alternatives as long as the parameters of endogenous variables are strongly identified in at least one of the clusters. We further develop a wild bootstrap Anderson-Rubin (AR) test for the full-vector inference and show that it controls size asymptotically up to a small error even under weak or partial identification for all clusters. We illustrate the good finite-sample performance of the new inference methods using simulations and provide an empirical application to a well-known dataset about US local labour markets.
