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Beta-Product Dependent Pitman-Yor Processes for Bayesian Inference

  • Federico Bassetti

    (Department of Mathematics, University of Pavia)

  • Roberto Casarin


    (Department of Economics, University of Venice C� Foscari)

  • Fabrizio Leisen

    (Department of Statistics, Universidad Carlos III de Madrid)

Multiple time series data may exhibit clustering over time and the clustering effect may change across different series. This paper is motivated by the Bayesian non�parametric modelling of the dependence between clustering effects in multiple time series analysis. We follow a Dirichlet process mixture approach and define a new class of multivariate dependent Pitman-Yor processes (DPY). The proposed DPY are represented in terms of a vector of stick-breaking processes which determines dependent clustering structures in the time series. We follow a hierarchical specification of the DPY base measure to accounts for various degrees of information pooling across the series. We discuss some theoretical properties of the DPY and use them to define Bayesian non�parametric repeated measurement and vector autoregressive models. We provide efficient Monte Carlo Markov Chain algorithms for posterior computation of the proposed models and illustrate the effectiveness of the method with a simulation study and an application to the United States and the European Union business cycles.

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Paper provided by Department of Economics, University of Venice "Ca' Foscari" in its series Working Papers with number 2013:13.

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Length: 33 pages
Date of creation: 2013
Date of revision:
Handle: RePEc:ven:wpaper:2013:13
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