Bayesian Inference for Time Series with Stable Innovations

Zuqiang Qiou and Nalini Ravishanker

Journal of Time Series Analysis, 1998, vol. 19, issue 2, 235-249

Abstract: This paper describes Bayesian inference for a linear time series model with stable innovations. An advantage of the Bayesian approach is that it enables the simultaneous estimation of the parameters characterizing the stable law and the parameters of the linear autoregressive moving‐average model. Our approach uses a Metropolis–Hastings algorithm to generate samples from the joint posterior distribution of all the parameters and subsequent inference is based on these samples. We illustrate our approach using data simulated from three linear processes with stable innovations and a real data set

Date: 1998
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://doi.org/10.1111/1467-9892.00088

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:2:p:235-249

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 ().