A New Approach to Factor Vector Autoregressive Estimation with an Application to Large-Scale Macroeconometric Modelling

In this paper a new approach to factor vector autoregressive estimation, based on Stock and Watson (2005), is introduced. Relative to the Stock-Watson approach, the proposed method has the advantage of allowing for a more clear-cut interpretation of the global factors, as well as for the identi.cation of all idiosyncratic shocks. Moreover, it shares with the Stock-Watson approach the advantage of using an iterated procedure in estimation, recovering, asymptotically, full effciency, and also allowing the imposition of appropriate restrictions concerning the lack of Granger causality of the variables versus the factors. Finally, relative to other available methods, our modelling approach has the advantage of allowing for the joint modelling of all variables, without resorting to long-run forcing hypotheses. An application to large-scale macroeconometric modelling is also provided.