The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance for US time series with the most promising existing alternatives, namely, factor models, large scale Bayesian VARs, and multivariate boosting. Speci…cally, we focus on classical reduc...

Multivariate time-series modeling and forecasting is an important problem with numerous applications. Traditional approaches such as VAR (vector auto-regressive) models and more recent approaches such as RNNs (recurrent neural networks) are indispensable tools in modeling time-series data. In many multivariate time series modeling problems, there is usually a significant linear dependency compo...

5 Existing research provides no systematic, limited information procedure for handling 6 non-invertibility, despite the well-known inference problem it causes as well as its 7 presence in many types of dynamic systems. Non-invertibility means that structural 8 shocks cannot be recovered from a history of observed variables. It arises from a form 9 of delayed responses due to, among other things...