Rates of expansions for functional estimators

Yulia Kotlyarova, Marcia M.A. Schafgans and Victoria Zinde-Walsh

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

Abstract: In this paper, we summarize results on convergence rates of various kernel based non- and semiparametric estimators, focusing on the impact of insufficient distributional smoothness, possibly unknown smoothness and even non-existence of density. In the presence of a possible lack of smoothness and the uncertainty about smoothness, methods of safeguarding against this uncertainty are surveyed with emphasis on nonconvex model averaging. This approach can be implemented via a combined estimator that selects weights based on minimizing the asymptotic mean squared error. In order to evaluate the finite sample performance of these and similar estimators we argue that it is important to account for possible lack of smoothness.

Keywords: combined estimator; convergence rates; degree of smoothness; kernel based estimation; model averaging; nonparametric estimation (search for similar items in EconPapers)
JEL-codes: J1 N0 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2021-11-18
New Economics Papers: this item is included in nep-ecm
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Published in Journal of Quantitative Economics, 18, November, 2021, 19, pp. 121-139. ISSN: 0971-1554

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http://eprints.lse.ac.uk/113436/ Open access version. (application/pdf)

Related works:
Journal Article: Rates of Expansions for Functional Estimators (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:113436

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