Detection of jumps by wavelets in a heteroscedastic autoregressive model
AbstractWavelets are applied to detect the jumps in a heteroscedastic autoregressive model. The empirical wavelet coefficients are defined respectively for the conditional mean and the conditional variance of the model. It is shown that the wavelet coefficients exhibit high peaks near the jump points, based on which a procedure is developed to identify and then to locate the jumps. All estimators are shown to be consistent.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 52 (2001)
Issue (Month): 4 (May)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Yujiao Yang & Qiongxia Song, 2014. "Jump detection in time series nonparametric regression models: a polynomial spline approach," Annals of the Institute of Statistical Mathematics, Springer, vol. 66(2), pages 325-344, April.
- Chen, Gongmeng & Choi, Yoon K. & Zhou, Yong, 2005. "Nonparametric estimation of structural change points in volatility models for time series," Journal of Econometrics, Elsevier, vol. 126(1), pages 79-114, May.
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