REAL TIME ESTIMATION IN LOCAL POLYNOMIAL REGRESSION, WITH APPLICATION TO TREND-CYCLE ANALYSIS

• 成果类型: Article
• 署名作者: Proietti, Tommaso; Luati, Alessandra
• 署名单位: University of Rome Tor Vergata; University of Bologna
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/08-AOAS195
• 发表日期: 2008
• 页码: 1523-1553
• 关键词: Filters

摘要

The paper focuses on the adaptation of local polynomial filters at the end of the sample period. We show that for real time estimation of signals (i.e., exactly at the boundary of the time support) we cannot rely on the automatic adaptation of the local polynomial smoothers, since the direct real time filter turns out to be strongly localized, and thereby yields extremely volatile estimates. As an alternative, we evaluate a general family of asymmetric filters that minimizes the mean square revision error subject to polynomial reproduction constraints; in the case of the Henderson filter it nests the well-known Musgrave's surrogate filters. The class of filters depends on unknown features of the series such as the slope and the curvature of the underlying signal, which can be estimated from the data. Several empirical examples illustrate the effectiveness of our proposal.