I introduce a factor structure on the parameters of a Bayesian TVP-VAR to reduce the dimension of the model's state space. To further limit the scope of over-fitting the estimation of the factor loadings uses a new generation of shrinkage priors. A Monte Carlo study illustrates the ability of...

Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory....

Dimension reduction techniques for functional data analysis model and approximate smooth random functions by lower dimensional objects. In many applications the focus of interest lies not only in dimension reduction but also in the dynamic behaviour of the lower dimensional objects. The most...