This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require...

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...

This double-issue contains 11 papers invited for the first special issue on “Computational methods for Russian economic and financial modelling”. It was an attempt to explore and bring together practical, state-of-the-art applications of computational techniques with a particular focus on...

In this paper, we study the evolution of US divorce rates across states, from 1956 to 1998. By using a cluster algorithm, we identify different groups of states that converge (or diverge) with (or from) each other in the growth of their divorce rates. We find strong support for the club...

The time-series dynamics of short-term interest rates are important as they are a key input into pricing models of the term structure of interest rates. In this paper we extend popular discrete time short-rate models to include Markov switching of infinite dimension. This is a Bayesian...

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a...