Beta-Product Dependent Pitman-Yor Processes for Bayesian Inference

Federico Bassetti, Roberto Casarin and Fabrizio Leisen
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Federico Bassetti: Department of Mathematics, University of Pavia
Fabrizio Leisen: Department of Statistics, Universidad Carlos III de Madrid

No 2013:13, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: 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.

Keywords: Bayesian non�parametrics; Dirichlet process; Panel Time-series non�parametrics; Pitman-Yor process; Stick-breaking process; Vector autoregressive process; Repeated measurements non-parametrics (search for similar items in EconPapers)
JEL-codes: C11 C14 C32 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2013
New Economics Papers: this item is included in nep-ecm and nep-ore
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Related works:
Journal Article: Beta-product dependent Pitman–Yor processes for Bayesian inference (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:ven:wpaper:2013:13

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