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Activity Number: 212
Type: Contributed
Date/Time: Monday, August 3, 2009 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract - #305024
Title: Detecting and Testing Change Points in Nonparametric Models Based on Series Estimation Methods
Author(s): Yingxing Li*+ and Haiqiang Chen
Companies: Cornell University and Cornell University
Address: Dept. of Statistical Science, Ithaca, NY, 14850,
Keywords: structural change points ; nonparametric ; series estimation
Abstract:

This paper considers detecting and testing structural change points in nonparametric models using series estimation methods. We allow multiple change points in the model and show that they could be estimated consistently by a sequential method. We derive the asymptotic distribution of our estimators. Unlike kernel estimation, ours has the same convergence rate as that in parametric change-point models. We further design tests on the existence of change points. The results are demonstrated by Monte Carlo simulations and an application is provided.

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