Semiparametric estimation by model selection for locally stationary processes

• 成果类型: Article
• 署名作者: Van Bellegem, Sebastien; Dahlhaus, Rainer
• 署名单位: Universite Catholique Louvain; Ruprecht Karls University Heidelberg
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2006.00564.x
• 发表日期: 2006
• 页码: 721-746
• 关键词: time-series models; Adaptive estimation; bounds

摘要

Over recent decades increasingly more attention has been paid to the problem of how to fit a parametric model of time series with time-varying parameters. A typical example is given by autoregressive models with time-varying parameters. We propose a procedure to fit such time-varying models to general non-stationary processes. The estimator is a maximum Whittle likelihood estimator on sieves. The results do not assume that the observed process belongs to a specific class of time-varying parametric models. We discuss in more detail the fitting of time-varying AR(p) processes for which we treat the problem of the selection of the order p, and we propose an iterative algorithm for the computation of the estimator. A comparison with model selection by Akaike's information criterion is provided through simulations.