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.
"Time-varying nonlinear regression models: Nonparametric estimation and model selection." Ann. Statist. 43 (2) 741 - 768, April 2015. https://doi.org/10.1214/14-AOS1299