Griffin, J.E. and Steel, Mark F.J. (2011) Stick-Breaking Autoregressive Processes. Journal of Econometrics, 162 (2). pp. 383-396. ISSN 0304-4076.
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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.
|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:||06 Jun 2012 11:20|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/29591 (The current URI for this page, for reference purposes)|
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