A New Test of Linearity of Time Series Based on the Bispectrum

Gy. Terdik and J. Math

Journal of Time Series Analysis, 1998, vol. 19, issue 6, 737-753

Abstract: The linearity of a time series is tested by use of the bispectrum. We define the time series to be linear if the best predictor is linear. The bispectrum is estimated by stretching the data and smoothing by the Subba Rao–Gabr optimal window. The null hypothesis tested here is that the best predictor is linear against the alternative that the best predictor is quadratic. It turns out that the test statistic is asymptotically χ2‐distributed under the hypothesis that the time series is linear. The results are demonstrated using simulated and real data.

Date: 1998
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/1467-9892.00120

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:19:y:1998:i:6:p:737-753

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 ().