  Shrinkage for time-varying parameter (TVP) models is investigated within a
Bayesian framework, with the aim to automatically reduce time-varying
parameters to static ones, 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 developed, exploiting boosting
based on the ancillarity-sufficiency interweaving strategy. The method is
applicable both to TVP models for univariate as well as multivariate time
series. Applications include a TVP generalized Phillips curve for EU area
inflation modelling and a multivariate TVP Cholesky stochastic volatility model
for joint modelling of the returns from the DAX-30 index.

Methodology;Applications;Computation