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April 2015 Time-varying nonlinear regression models: Nonparametric estimation and model selection
Ting Zhang, Wei Biao Wu
Ann. Statist. 43(2): 741-768 (April 2015). DOI: 10.1214/14-AOS1299


This paper considers a general class of nonparametric time series regression models where the regression function can be time-dependent. We establish an asymptotic theory for estimates of the time-varying regression functions. For this general class of models, an important issue in practice is to address the necessity of modeling the regression function as nonlinear and time-varying. To tackle this, we propose an information criterion and prove its selection consistency property. The results are applied to the U.S. Treasury interest rate data.


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Ting Zhang. Wei Biao Wu. "Time-varying nonlinear regression models: Nonparametric estimation and model selection." Ann. Statist. 43 (2) 741 - 768, April 2015.


Published: April 2015
First available in Project Euclid: 3 March 2015

zbMATH: 1312.62046
MathSciNet: MR3319142
Digital Object Identifier: 10.1214/14-AOS1299

Primary: 62G05 , 62G08
Secondary: 62G20

Keywords: information criterion , local linear estimation , nonparametric model selection , nonstationary processes , time-varying nonlinear regression models

Rights: Copyright © 2015 Institute of Mathematical Statistics


Vol.43 • No. 2 • April 2015
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