An omnibus noise filter |
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Authors: | Claudio Morana |
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Institution: | (1) Facoltà di Economia, Dipartimento di Scienze Economiche e Metodi Quantitativi, Università del Piemonte Orientale, Via Perrone 18, 28100 Novara, Italy;(2) International Centre for Economic Research (ICER, Torino), Torino, Italy |
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Abstract: | A new noise filtering approach, based on flexible least squares (FLS) estimation of an unobserved component local level model,
is introduced. The proposed FLS filter has been found to perform well in Monte Carlo analysis, independently of the persistence
properties of the data and the size of the signal to noise ratio, ouperforming in general even the Wiener Kolmogorov filter,
which, theoretically, is a minimum mean square estimator. Moreover, a key advantage of the proposed filter, relatively to
available competitors, is that any persistence property of the data can be handled, without any pretesting, being computationally
fast and not demanding, and easy to be implemented as well.
This paper was partially written when the author was visiting the Department of Economics at Michigan State University. The
author gratefully acknowledges funding from the Fulbright Commission and Michigan State University for hospitality. Many thanks
to two anonymous referee and the associate editor for very constructive comments. |
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Keywords: | Signal– noise decomposition Long memory Structural breaks Flexible least squares Exchange rates volatility |
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