Beta-product dependent Pitman–Yor processes for Bayesian inference

Federico Bassetti, Roberto Casarin and Fabrizio Leisen

Journal of Econometrics, 2014, vol. 180, issue 1, 49-72

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 vectors of stick-breaking processes which determine dependent clustering structures in the time series. We follow a hierarchical specification of the DPY base measure to account 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 cycle.

Keywords: Bayesian non-parametrics; Dirichlet process; Panel vector autoregressive process; Pitman–Yor process; Stick-breaking process (search for similar items in EconPapers)
JEL-codes: C11 C14 C32 (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (28)

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Working Paper: Beta-Product Dependent Pitman-Yor Processes for Bayesian Inference (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:180:y:2014:i:1:p:49-72

DOI: 10.1016/j.jeconom.2014.01.007

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