Bayesian Inference of Autoregressive and Functional-Coefficient Moving Average Models

Hai-Bin Wang and Ping Wu

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 3, 453-467

Abstract: Based on free-knot splines techniques, we develop a fully Bayesian method to make inference about the autoregressive and functional-coefficient moving-average models, including estimation and prediction. We approximate different functional-coefficients by polynomial splines with different orders to adapt to different smoothness. To make the estimation and prediction robust, we assign heavy-tailed student-t priors on the coefficients of both the splines and the autoregressive terms. The posterior predictive distribution is derived from a Bayesian model average over all of the possible models. The proposed method is demonstrated by both simulated and real data examples.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2012.742110 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:44:y:2015:i:3:p:453-467

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2012.742110

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().