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Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce

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Volume 19, Issue 5


Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks

Nima Nonejad
  • Corresponding author
  • Aarhus University and Creates – Department of Economics and Business, Fuglesangs Alle 4, Aarhus 8210, Aarhus V, Denmark
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  • Other articles by this author:
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Published Online: 2015-04-21 | DOI: https://doi.org/10.1515/snde-2014-0043


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 stochastic volatility models with increasing complexity, including leverage and in mean effects. We provide applications that demonstrate the flexibility of PG-AS under these different circumstances and justify applying it in practice. We also combine discrete structural breaks within the stochastic volatility model framework. For instance, we model changing time series characteristics of monthly postwar US core inflation rate using a structural break autoregressive fractionally integrated moving average (ARFIMA) model with stochastic volatility. We allow for structural breaks in the level, long and short-memory parameters with simultaneous breaks in the level, persistence and the conditional volatility of the volatility of inflation.

This article offers supplementary material which is provided at the end of the article.

Keywords: ancestor sampling; Bayes; particle filtering; structural breaks

JEL Classification:: C11; C22; C52; C63


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About the article

Corresponding author: Nima Nonejad, Aarhus University and Creates – Department of Economics and Business, Fuglesangs Alle 4, Aarhus 8210, Aarhus V, Denmark, e-mail:

Published Online: 2015-04-21

Published in Print: 2015-12-01

Citation Information: Studies in Nonlinear Dynamics & Econometrics, Volume 19, Issue 5, Pages 561–584, ISSN (Online) 1558-3708, ISSN (Print) 1081-1826, DOI: https://doi.org/10.1515/snde-2014-0043.

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