This chapter surveys the importance of reduced rank regression techniques (RRR) for modelling economic and financial time series. We mainly focus on models that are capable to reproduce the presence of common dynamics among variables such as the serial correlation common feature and the...

For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA hereafter), we show that the presence of common cyclical features or cointegration leads to a reduction of the order of the implied univariate autoregressive-integrated-moving average (ARIMA...

We propose an approach for checking the data admissibility of non-stationary multivariate time series models (VAR or VARMA) through that of their implied individual ARIMA specifications. In particular we show that the presence of different kinds of common cyclical features restrictions, leading...