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Inference on distribution functions under measurement error

Karun Adusumilli, Daisies Kurisu, Taisuke Otsu and Yoon-Jae Whang

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This paper is concerned with inference on the cumulative distribution function (cdf) FX∗ in the classical measurement error model X = X∗ + ε. We consider the case where the density of the measurement error ε is unknown and estimated by repeated measurements, and show validity of a bootstrap approximation for the distribution of the deviation in the sup-norm between the deconvolution cdf estimator and FX∗. We allow the density of ε to be ordinary or super smooth. We also provide several theoretical results on the bootstrap and asymptotic Gumbel approximations of the sup-norm deviation for the case where the density of ε is known. Our approximation results are applicable to various contexts, such as confidence bands for FX∗ and its quantiles, and for performing various cdf-based tests such as goodness-of-fit tests for parametric models of X∗, two sample homogeneity tests, and tests for stochastic dominance. Simulation and real data examples illustrate satisfactory performance of the proposed methods.

Keywords: measurement error; deconvolution; confidence band; stochastic dominance (search for similar items in EconPapers)
JEL-codes: J1 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2020-03-01
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Published in Journal of Econometrics, 1, March, 2020, 215(1), pp. 131 - 164. ISSN: 0304-4076

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http://eprints.lse.ac.uk/102692/ Open access version. (application/pdf)

Related works:
Journal Article: Inference on distribution functions under measurement error (2020) Downloads
Working Paper: Inference on distribution functions under measurement error (2017) Downloads
Working Paper: INFERENCE ON DISTRIBUTION FUNCTIONS UNDER MEASUREMENT ERROR Downloads
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