TIME-VARYING NONLINEAR REGRESSION MODELS: NONPARAMETRIC ESTIMATION AND MODEL SELECTION

• 成果类型: Article
• 署名作者: Zhang, Ting; Wu, Wei Biao
• 署名单位: Boston University; University of Chicago
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/14-AOS1299
• 发表日期: 2015
• 页码: 741-768
• 关键词: testing parameter constancy; coefficient models; kernel estimation; ASYMPTOTIC THEORY; longitudinal data; term structure; linear-models; bandwidth selection; DENSITY-ESTIMATION; confidence bands

摘要

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.