Multivariate functional-coefficient regression models for nonlinear vector time series data

• 成果类型: Article
• 署名作者: Jiang, Jiancheng
• 署名单位: University of North Carolina; University of North Carolina Charlotte
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu011
• 发表日期: 2014
• 页码: 689702
• 关键词: variable bandwidth; estimators; tests

摘要

Vector time series data are widely met in practice. In this paper we propose a multivariate functional-coefficient regression model with heteroscedasticity for modelling such data. A local linear smoother is employed to estimate the unknown coefficient matrices. Asymptotic normality of the proposed estimators is established, and bandwidth selection is considered. To deal with the co-integration commonly observed in financial markets, we propose an error-corrected multivariate functional-coefficient model. Simulations show that our proposed estimation procedures capture nonlinear structures of coefficients well. Analysis of United States interest rates illustrates the proposed methods.