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On the uniform convergence of deconvolution estimators from repeated measurement

Kurisu, Daisuke and Otsu, Taisuke (2020) On the uniform convergence of deconvolution estimators from repeated measurement. Econometric Theory. ISSN 1469-4360 (In Press)

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Abstract

This paper studies the uniform convergence rates of Li and Vuong’s (1998) non- parametric deconvolution estimator and its regularized version by Comte and Kappus (2015) for the classical measurement error model, where repeated noisy measurements on the error- free variable of interest are available. In contrast to Li and Vuong (1998), our assumptions allow unbounded supports for the error-free variable and measurement errors. Compared to Bonhomme and Robin (2010) specialized to the measurement error model, our assumptions do not require existence of the moment generating functions of the square and product of repeated measurements. Furthermore, by utilizing a maximal inequality for the multivariate normalized empirical characteristic function process, we derive the uniform convergence rates that are faster than the ones derived in these papers under such weaker conditions.

Item Type: Article
Official URL: https://www.cambridge.org/core/journals/econometri...
Additional Information: © 2020 Cambridge University Press
Divisions: Economics
Subjects: H Social Sciences > HB Economic Theory
Date Deposited: 30 Nov 2020 12:36
Last Modified: 01 Dec 2020 14:54
URI: http://eprints.lse.ac.uk/id/eprint/107533

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