Time-varying nonlinear regression models: Nonparametric estimation and model selection

The Annals of Statistics · 2015 · Volume 43 · Issue 2

Abstract

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.

Keywords

62G05, 62G08, 62G20, information criterion, local linear estimation, nonparametric model selection, nonstationary processes, time-varying nonlinear regression models

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