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Boundary modification in local polynomial regression*

Yuanhua Feng () and Bastian Schäfer
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Bastian Schäfer: Paderborn University

No 144, Working Papers CIE from Paderborn University, CIE Center for International Economics

Abstract: This paper discusses the suitable choice of the weighting function at a boundary point in local polynomial regression and introduces two new boundary modi- cation methods by adapting known ideas for generating boundary kernels. Now continuous estimates at endpoints are achievable. Under given conditions the use of those quite different weighting functions at an interior point is equivalent. At a boundary point the use of dierent methods will lead to different estimates. It is also shown that the optimal weighting function at the endpoints is a natural extension of one of the optimal weighting functions in the interior. Furthermore, it is shown that the most well known boundary kernels proposed in the literature can be generated by local polynomial regression using corresponding weighting functions. The proposals are particularly useful, when one-side smoothing or de- tection of change points in nonparametric regression are considered.

Keywords: Local polynomial regression; equivalent weighting methods; boundary modification; boundary kernels; finite sample property (search for similar items in EconPapers)
JEL-codes: C14 C51 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2021-08
New Economics Papers: this item is included in nep-ecm, nep-isf and nep-ore
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