Stick-Breaking Autoregressive Processes

Griffin, Jim E. and Steel, Mark F.J. (2011) Stick-Breaking Autoregressive Processes. Journal of Econometrics, 162 (2). pp. 383-396. ISSN 0304-4076. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1016/j.jeconom.2011.03.001

Abstract

This paper considers the problem of defining a time-dependent nonparametric prior for use in Bayesian nonparametric modelling of time series. A recursive construction allows the definition of priors whose marginals have a general stick-breaking form. The processes with Poisson–Dirichlet and Dirichlet process marginals are investigated in some detail. We develop a general conditional Markov Chain Monte Carlo (MCMC) method for inference in the wide subclass of these models where the parameters of the marginal stick-breaking process are nondecreasing sequences. We derive a generalised Pólya urn scheme type representation of the Dirichlet process construction, which allows us to develop a marginal MCMC method for this case. We apply the proposed methods to financial data to develop a semi-parametric stochastic volatility model with a time-varying nonparametric returns distribution. Finally, we present two examples concerning the analysis of regional GDP and its growth.

Item Type: Article
Uncontrolled keywords: Bayesian nonparametrics; Dirichlet process; Poisson–Dirichlet process; Time-dependent nonparametrics
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 30 May 2012 10:01
Last Modified: 21 May 2014 11:24
Resource URI: https://kar.kent.ac.uk/id/eprint/29591 (The current URI for this page, for reference purposes)
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