Nonlinear Spectral Density Estimation: Thresholding the Correlogram

12 Pages Posted: 21 Apr 2012

See all articles by Efstathios Paparoditis

Efstathios Paparoditis

University of Cyprus - Department of Mathematics and Statistics

Dimitris N. Politis

University of California, San Diego (UCSD) - Department of Mathematics

Date Written: May 2012

Abstract

Traditional kernel spectral density estimators are linear as a function of the sample autocovariance sequence. The purpose of this article is to propose and analyse two new spectral estimation methods that are based on the sample autocovariances in a nonlinear way. The rate of convergence of the new estimators is quantified, and practical issues such as bandwidth and/or threshold choice are addressed. The new estimators are also compared with traditional ones using flat‐top lag‐windows in a simulation experiment involving sparse time‐series models.

Keywords: Autocovariance matrix, flat‐top lag‐windows, kernel smoothing, sparsity, thresholding, wavelets

Suggested Citation

Paparoditis, Efstathios and Politis, Dimitris, Nonlinear Spectral Density Estimation: Thresholding the Correlogram (May 2012). Journal of Time Series Analysis, Vol. 33, Issue 3, pp. 386-397, 2012, Available at SSRN: https://ssrn.com/abstract=2042905 or http://dx.doi.org/10.1111/j.1467-9892.2011.00771.x

Efstathios Paparoditis (Contact Author)

University of Cyprus - Department of Mathematics and Statistics ( email )

P.O. Box 20537
1678 Nicosia
Cyprus
00357-2-338701 (Phone)
00357-2-339061 (Fax)

Dimitris Politis

University of California, San Diego (UCSD) - Department of Mathematics ( email )

9500 Gilman Drive
La Jolla, CA 92093-0112
United States
858-534-5861 (Phone)
858-534-5273 (Fax)

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