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An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series

John Maheu () and Yong Song ()

MPRA Paper from University Library of Munich, Germany

Abstract: This paper provides a feasible approach to estimation and forecasting of multiple structural breaks for vector autoregressions and other multivariate models. Due to conjugate prior assumptions we obtain a very efficient sampler for the regime allocation variable. A new hierarchical prior is introduced to allow for learning over different structural breaks. The model is extended to independent breaks in regression coefficients and the volatility parameters.Two empirical applications show the improvements the model has over benchmarks. In a macro application with 7 variables we empirically demonstrate the benefits from moving from a multivariate structural break model to a set of univariate structural break models to account for heterogeneous break patterns across data series.

Keywords: multivariate hierarchical prior; change point; forecasting (search for similar items in EconPapers)
JEL-codes: C1 C11 C32 C53 E32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac and nep-ore
Date: 2017-05
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https://mpra.ub.uni-muenchen.de/79211/1/MPRA_paper_79211.pdf original version (application/pdf)

Related works:
Journal Article: An efficient Bayesian approach to multiple structural change in multivariate time series (2018) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:79211

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