Department of Economics and Business Economics

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

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review


  • Nima Nonejad

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.

Original languageEnglish
JournalStudies in Nonlinear Dynamics and Econometrics (Online)
Pages (from-to)561-584
Number of pages24
Publication statusPublished - 1 Dec 2015

    Research areas

  • Ancestor sampling, Bayes, Particle filtering, Structural breaks

See relations at Aarhus University Citationformats

ID: 98074716