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December 2008 Real time estimation in local polynomial regression, with application to trend-cycle analysis
Tommaso Proietti, Alessandra Luati
Ann. Appl. Stat. 2(4): 1523-1553 (December 2008). DOI: 10.1214/08-AOAS195

Abstract

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.

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Tommaso Proietti. Alessandra Luati. "Real time estimation in local polynomial regression, with application to trend-cycle analysis." Ann. Appl. Stat. 2 (4) 1523 - 1553, December 2008. https://doi.org/10.1214/08-AOAS195

Information

Published: December 2008
First available in Project Euclid: 8 January 2009

zbMATH: 05505366
MathSciNet: MR2655670
Digital Object Identifier: 10.1214/08-AOAS195

Keywords: Henderson filter , Musgrave asymmetric filters , trend estimation

Rights: Copyright © 2008 Institute of Mathematical Statistics

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Vol.2 • No. 4 • December 2008
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