Sparse Autoregressive based Estimation for Long-memory Models

摘要：

Many economic, financial and engineering time series data exhibit long-term persistence. The autoregressive fractionally integrated moving average (ARFIMA) process is characterized by a slowly decaying autocorrelation function and arises as a popular statistical tool for modeling long memory time series. After years of development on the semipara-metric two-stage direct estimation of ARFIMA, recently there has been a considerable interest in the long-order autoregressive (AR) approximation, as it is observed to be simple and effective. This paper proposes a sparse AR approximation to the ARFIMA process based on penalized conditional likelihood. Simulation study shows that the proposed method leads to better model flexibility and prediction accuracy. Finally, we apply the method to analyze a foreign exchange rate data and the result is very satisfactory.

关键词： ARFIMA autoregressive approximation for-eign exchange-rate long-memory penalized conditional like-lihood sparse

作者: Yan Sun

作者单位: Department of Mathematics & Statistics Utah State University Logan, Utah 84322-3900 Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio 45221-0025

会议类型: 国际会议

会议名称: The Tenth International Conference on Information and Management Sciences(IMS)(第十届信息与管理科学国际会议)

会议地点: 拉萨

会议语种:英文

页码: 300-307

在线出版日期: 2011-08-06（万方平台首次上网日期，不代表论文的发表时间）