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.
Signal–noise decomposition Long memory Structural breaks Flexible least squares Exchange rates volatility
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