Dilation bootstrap

Alfred Galichon, Marc Henry

    Research output: Contribution to journalArticlepeer-review


    We propose a methodology for combining several sources of model and data incompleteness and partial identification, which we call Composition Theorem. We apply this methodology to the construction of 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 et al. (2011).

    Original languageEnglish (US)
    Pages (from-to)109-115
    Number of pages7
    JournalJournal of Econometrics
    Issue number1
    StatePublished - Nov 2013


    • Dilation bootstrap
    • Optimal matching
    • Partial identification
    • Quantile process

    ASJC Scopus subject areas

    • Economics and Econometrics


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