Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks

Nima Nonejad

MPRA Paper from University Library of Munich, Germany

Abstract: Particle Gibbs with ancestor sampling (PG-AS) is a new tool in the family of sequential Monte Carlo methods. We apply PG-AS to the challenging class of unobserved component time series models and demonstrate its flexibility under different circumstances. We also combine discrete structural breaks within the unobserved component model framework. We do this by modeling and forecasting time series characteristics of postwar US inflation using a long memory autoregressive fractionally integrated moving average model with stochastic volatility where we allow for structural breaks in the level, long and short memory parameters contemporaneously with breaks in the level, persistence and the conditional volatility of the volatility of inflation.

Keywords: Ancestor sampling; Bayes; Particle filtering; Structural breaks (search for similar items in EconPapers)
JEL-codes: C11 C22 C52 C63 (search for similar items in EconPapers)
Date: 2014-05-01
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:55664

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