Rates of Expansions for Functional Estimators

Yulia Kotlyarova, Marcia M. A. Schafgans and Victoria Zinde-Walsh ()
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Marcia M. A. Schafgans: London School of Economics
Victoria Zinde-Walsh: McGill University and CIREQ

Journal of Quantitative Economics, 2021, vol. 19, issue 1, No 8, 139 pages

Abstract: 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: Nonparametric estimation; Kernel based estimation; Model averaging; Combined estimator; Convergence rates; Degree of smoothness (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s40953-021-00266-8

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