Flexible model comparison of unobserved components models using particle Gibbs with ancestor sampling

•Unobserved components models with stochastic volatility (SV) effects are widely used to model inflation rates.•However, formal model comparison using Gibbs sampling is difficult.•We show that PG-AS provides a flexible framework for estimation and model comparison.•We provide applications using US and UK data, comparing different models.•The model with time-varying SV in mean effects performs best.

In this paper, we show that particle Gibbs with ancestor sampling (PG-AS) provides a unified and flexible framework for estimation and model comparison of unobserved components models with time-varying volatility effects, which are widely used in inflation rate modeling.