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Real Time Estimation in Local Polynomial Regression, with Application to Trend-Cycle Analysis

Tommaso Proietti and Alessandra Luati
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Alessandra Luati: University of Bologna

No 112, CEIS Research Paper from Tor Vergata University, CEIS

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 localised, and thereby yields extremely volatile estimates. As an alternative we evaluate a general family of asymmetric filters that minimises 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. We also discuss the merits of using a nearest neighbour bandwidth as opposed to a fixed bandwidth for improving the quality of the approximation.

Keywords: Henderson; filter.; Trend; estimation.; Nearest; Neighbour; Bandwidth.; Musgrave; asymmetric; filters (search for similar items in EconPapers)
Pages: 26 pages
Date: 2008-07-14, Revised 2008-07-14
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:rtv:ceisrp:112

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