NON AND SEMI-PARAMETRIC ESTIMATION IN MODELS WITH UNKNOWN SMOOTHNESS

Many asymptotic results for kernel-based estimators were established under some smoothness assumption on density. For cases where smoothness assumptions that are used to derive unbiasedness or asymptotic rate may not hold we propose a combined estimator that could lead to the best available rate without knowledge of density smoothness. A Monte Carlo example confirms good performance of the combined estimator.

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Year of publication:	2006-09
Authors:	Kotlyarova, Yulia ; Zinde-Walsh, Victoria
Institutions:	Department of Economics, McGill University

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Extent:	application/pdf
Series:	Departmental Working Papers.
Type of publication:	Book / Working Paper
Language:	English
Notes:	10 pages
Classification:	C14 - Semiparametric and Nonparametric Methods
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005698057