Dilatation Bootstrap : a methodology for constructing confidence regions with partially identified models
Journal of Econometrics
NL : Elsevier
109 - 115 p.
In press (accepted manuscript).
Partial identification, Dilation bootstrap, Quantile process, Optimal matching
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. It requires bootstrapping the quantile process for univariate data and a novel generalization of the latter to higher dimensions. Once the dilation is chosen to control the confidence level, the unknown true distribution of the observed data can be replaced by the known empirical distribution and confidence regions can then be obtained as in Galichon and Henry (2011) and Beresteanu, Molchanov and Molinari (2011).