Dilation bootstrap

Journal of Econometrics, 2013, vol. 177, issue 1, 109-115

Abstract: 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).

Keywords: Partial identification; Dilation bootstrap; Quantile process; Optimal matching (search for similar items in EconPapers)
JEL-codes: C15 C31 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (3)

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Working Paper: Dilation bootstrap (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:177:y:2013:i:1:p:109-115

DOI: 10.1016/j.jeconom.2013.07.001

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