Inference on Directionally Differentiable Functions

This paper studies an asymptotic framework for conducting inference on parameters of the form ( 0), where is a known directionally dierentiable function and 0 is estimated by ^ n. In these settings, the asymptotic distribution of the plug-in estimator ( ^ n) can be readily derived employing existing extensions to the Delta method. We show, however, that the \standard" bootstrap is only consistent under overly stringent conditions { in particular we establish that dierentiability of is a necessary and sucient condition for bootstrap consistency whenever the limiting distribution of ^ n is Gaussian. An alternative resampling scheme is proposed which remains consistent when the bootstrap fails, and is shown to provide local size control under restrictions on the directional derivative of . We illustrate the utility of our results by developing a test of whether a Hilbert space valued parameter belongs to a convex set { a setting that includes moment inequality problems and certain tests of shape restrictions as special cases.