ASYMPTOTIC INFERENCE FOR NON‐INVERTIBLE MOVING‐AVERAGE TIME SERIES

Ngai Hang Chan and Ruey S. Tsay

Journal of Time Series Analysis, 1996, vol. 17, issue 1, 1-17

Abstract: Abstract. This paper is concerned with statistical inference of nonstationary and non‐invertible autoregressive moving‐average (ARMA) processes. It makes use of the fact that a derived process of an ARMA(p, q) model follows an AR(q) model with an autoregressive (AR) operator equivalent to the moving‐average (MA) part of the original ARMA model. Asymptotic distributions of least squares estimates of MA parameters based on a constructed derived process are obtained as corresponding analogs of a nonstationary AR process. Extensions to the nearly non‐invertible models are considered and the limiting distributions are obtained as functionals of stochastic integrals of Brownian motions and Ornstein‐Uhlenbeck processes. For application, a two‐stage procedure is proposed for testing unit roots in the MA polynomial. Examples are given to illustrate the application.

Date: 1996
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/j.1467-9892.1996.tb00261.x

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:17:y:1996:i:1:p:1-17

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 ().