Adaptive jump-preserving estimates in varying-coefficient models

Yan-Yong Zhao, Jin-Guan Lin, Xing-Fang Huang and Hong-Xia Wang

Journal of Multivariate Analysis, 2016, vol. 149, issue C, 65-80

Abstract: Varying-coefficient models are very important tools to explore the hidden structure between the response variable and its predictors. This article focuses on the estimation of varying-coefficient models with discontinuous coefficient functions. Based on local linear smoothing and jump-preserving regression techniques, an adaptive jump-preserving (AJP) estimation procedure is proposed to estimate the coefficient functions with jumps. This method can automatically accommodate possible jumps of the coefficient functions without knowing the number and locations of jump points or performing any hypothesis tests. Under some mild conditions, the asymptotical properties of the resulting estimators can be established. Furthermore, several numerical studies are conducted to evaluate the finite sample performance of the proposed methodologies. Finally, an application to Australia consumer price index (CPI) data illustrates the validity of the proposed techniques.

Keywords: Adaptive jump-preserving estimation; Asymptotic properties; Local linear smoothing; Varying-coefficient models (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:149:y:2016:i:c:p:65-80

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DOI: 10.1016/j.jmva.2016.03.005

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