Bayesian Models for Non‐linear Autoregressions

Peter Müller, Mike West and Steven MacEachern

Journal of Time Series Analysis, 1997, vol. 18, issue 6, 593-614

Abstract: We discuss classes of Bayesian mixture models for nonlinear autoregressive times series, based on developments in semiparametric Bayesian density estimation in recent years. The development involves formal classes of multivariate discrete mixture distributions, providing flexibility in modeling arbitrary nonlinearities in time series structure and a formal inferential framework within which to address the problems of inference and prediction. The models relate naturally to existing kernel and related methods, threshold models and others, although they offer major advances in terms of parameter estimation and predictive calculations. Theoretic al and computational aspects are developed here, the latter involving efficient simulation of posterior and predictive distributions. Various examples illustrate our perspectives on identification and inference using this mixture approach

Date: 1997
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/1467-9892.00070

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:18:y:1997:i:6:p:593-614

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 ().