A new non‐parametric cross‐spectrum estimator

Evangelos E. Ioannidis

Journal of Time Series Analysis, 2022, vol. 43, issue 5, 808-827

Abstract: A new non‐parametric estimator of the cross‐spectrum of a bivariate stationary time series is proposed, which is non‐quadratic in the observations. This estimator is an extension of the Capon‐estimator of the spectrum of a univariate time series. The proposed estimator is designed so as to cope with the leakage effect induced by strong peaks of the marginal spectra by utilizing adaptive windowing. We study the asymptotic bias and covariance structure of the proposed estimator and prove a central limit theorem for its distribution. We also obtain a result of independent importance for the consistency rate of the cross‐covariance matrix of the two series. The performance of the estimator in comparison to more traditional ones is demonstrated in a simulation study under a model exhibiting extreme characteristics, such as strong peaks in its marginal spectra. Finally, the estimator is used to judge the fit of a VAR(p) model in a real data example.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/jtsa.12639

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:43:y:2022:i:5:p:808-827

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().