Stick-breaking autoregressive processes
Griffin, Jim E. and Steel, Mark F. J.. (2011) Stick-breaking autoregressive processes. Journal of Econometrics, Vol.162 (No.2). pp. 383-396. ISSN 0304-4076Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.jeconom.2011.03.001
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 Polya 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. (C) 2011 Elsevier B.V. All rights reserved.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||Faculty of Science > Statistics|
|Library of Congress Subject Headings (LCSH):||Bayesian statistical decision theory, Nonparametric statistics|
|Journal or Publication Title:||Journal of Econometrics|
|Publisher:||Elsevier BV * North-Holland|
|Official Date:||June 2011|
|Page Range:||pp. 383-396|
|Access rights to Published version:||Restricted or Subscription Access|
Carlin, B.P., Gelfand, A.E., Smith, A.F.M., 1992. Hierarchical Bayesian analysis of
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