Higher Order Properties of the Wild Bootstrap Under Misspecification

We examine the higher order properties of the wild bootstrap (Wu, 1986) in a linear regression model with stochastic regressors. We find that the ability of the wild bootstrap to provide a higher order refinement is contingent upon whether the errors are mean independent of the regressors or merely uncorrelated. In the latter case, the wild bootstrap may fail to match some of the terms in an Edgeworth expansion of the full sample test statistic, potentially leading to only a partial refinement (Liu and Singh, 1987). To assess the practical implications of this result, we conduct a Monte Carlo study contrasting the performance of the wild bootstrap with the traditional nonparametric bootstrap.