Achieving shrinkage in a time-varying parameter model framework

Shrinkage for time-varying parameter (TVP) models is investigated within a Bayesian
framework, with the aim to automatically reduce time-varying Parameters to staticones,
if the model is overfitting. This is achieved through placing the double gamma shrinkage
prior on the process variances. An efficient Markov chain Monte Carlo scheme is devel-
oped, exploiting boosting based on the ancillarity-sufficiency interweaving strategy. The
method is applicable both to TVP models for univariate a swell as multivariate time series.
Applications include a TVP generalized Phillips curve for EU area inflation modeling and
a multivariate TVP Cholesky stochastic volatility model for joint modeling of the Returns
from the DAX-30index.