Stick-breaking autoregressive processes

Jim Griffin and Mark Steel

Journal of Econometrics, 2011, vol. 162, issue 2, 383-396

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.

Keywords: Bayesian; nonparametrics; Dirichlet; process; Poisson-Dirichlet; process; Time-dependent; nonparametrics (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (33)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:162:y:2011:i:2:p:383-396

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