Description Length Based Signal Detection in singular Spectrum Analysis

Atikur Khan and Donald Poskitt

No 13/10, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper provides an information theoretic analysis of the signal-noise separation problem in Singular Spectrum Analysis. We present a signal-plus-noise model based on the Karhunen-Loève expansion and use this model to motivate the construction of a minimum description length criterion that can be employed to select both the window length and the signal. We show that under very general regularity conditions the criterion will identify the true signal dimension with probability one as the sample size increases, and will choose the smallest window length consistent with the Whitney embedding theorem. Empirical results obtained using simulated and real world data sets indicate that the asymptotic theory is reflected in observed behaviour, even in relatively small samples.

Keywords: Karhunen-Loève expansion; minimum description length; signal-plus-noise model; Singular Spectrum Analysis; embedding (search for similar items in EconPapers)
JEL-codes: C14 C22 C52 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2010-05-24
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (4)

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