Dilation Bootstrap A methodology for constructing confidence regions with partially identified models

We propose a methodology for constructing confidence regions with partially identified models of general form. The region is obtained by inverting a test of internal consistency of the econometric structure. We develop a dilation bootstrap methodology to deal with sampling uncertainty without reference to the hypothesized economic structure, and apply a duality principle to reduce the dimensionality of the remaining deterministic problem. As a result, the confidence region becomes easily computable, and the methodology can be applied to the estimation of models with sample selection, censored observations and to games with multiple equilibria. JEL Classification: C10, C12, C13, C15, C34, C52, C61.