Innovations algorithm asymptotics for periodically stationary time series with heavy tails

4Citations
Citations of this article
4Readers
Mendeley users who have this article in their library.

Abstract

The innovations algorithm can be used to obtain parameter estimates for periodically stationary time series models. In this paper we compute the asymptotic distribution for these estimates in the case where the underlying noise sequence has infinite fourth moment but finite second moment. In this case, the sample covariances on which the innovations algorithm are based are known to be asymptotically stable. The asymptotic results developed here are useful to determine which model parameters are significant. In the process, we also compute the asymptotic distributions of least squares estimates of parameters in an autoregressive model. © 2007 Elsevier Inc. All rights reserved.

Cite

CITATION STYLE

APA

Anderson, P. L., Kavalieris, L., & Meerschaert, M. M. (2008). Innovations algorithm asymptotics for periodically stationary time series with heavy tails. Journal of Multivariate Analysis, 99(1), 94–116. https://doi.org/10.1016/j.jmva.2007.02.005

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free