The Annals of Statistics
- Ann. Statist.
- Volume 43, Number 2 (2015), 741-768.
Time-varying nonlinear regression models: Nonparametric estimation and model selection
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.
Ann. Statist., Volume 43, Number 2 (2015), 741-768.
First available in Project Euclid: 3 March 2015
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Zhang, Ting; Wu, Wei Biao. Time-varying nonlinear regression models: Nonparametric estimation and model selection. Ann. Statist. 43 (2015), no. 2, 741--768. doi:10.1214/14-AOS1299. https://projecteuclid.org/euclid.aos/1425398507
- Supplementary material: Additional technical proofs. This supplement contains technical proofs of Lemmas 6.6 and 6.7, Proposition 3.1 and Theorems 3.6 and 3.7.