semiparametric estimators
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Author(s):  
Yulia Kotlyarova ◽  
Marcia M. A. Schafgans ◽  
Victoria Zinde-Walsh

AbstractIn 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.


Bernoulli ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 557-586
Author(s):  
Eric Beutner ◽  
Laurent Bordes ◽  
Laurent Doyen

Econometrica ◽  
2018 ◽  
Vol 86 (3) ◽  
pp. 955-995 ◽  
Author(s):  
Matias D. Cattaneo ◽  
Michael Jansson

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