A Bayesian Non‐Parametric Dynamic AR Model for Multiple Time Series Analysis. (8th February 2016)

• Record Type: Journal Article
• Authors: Nieto‐Barajas, Luis E.; Quintana, Fernando A.
• Is Part Of: Journal of time series analysis. Volume 37:Number 5(2016:Sep.)
• Page Start: 675
• Page End: 689
• Publication Date: 2016-02-08
• Subjects: Bayes non‐parametrics -- beta process -- latent variables -- dependent Polya tree -- median regression -- time series analysis; Time-series analysis -- Periodicals; 519.232
• DOI: 10.1111/jtsa.12182
• Languages: English
• ISSNs: 0143-9782

Abstract

Abstract : In this article, we propose a Bayesian non‐parametric model for the analysis of multiple time series. We consider an autoregressive structure of order p for each of the series and borrow strength across the series by considering a common error population that is also evolving in time. The error populations (distributions) are assumed non‐parametric whose law is based on a series of dependent Polya trees with zero median. This dependence is of order q and is achieved via a dependent beta process that links the branching probabilities of the trees. We study the prior properties and show how to obtain posterior inference. The model is tested under a simulation study and is illustrated with the analysis of the economic activity index of the 32 states of Mexico.