paper proposes a factor augmented autoregressive distributed lag (FADL)
framework for analyzing the dynamic effects of common and idiosyncratic shocks.
We first estimate the common shocks from a large panel of data with a strong
factor structure. Impulse responses are then obtained from an autoregression,
augmented with a distributed lag of the estimated common shocks. The approach
has three distinctive features. First, identification restrictions, especially
those based on recursive or block recursive ordering, are very easy to impose.
Second, the dynamic response to the common shocks can be constructed for
variables not necessarily in the panel. Third, the restrictions imposed by the
factor model can be tested. The relation to other identification schemes used
in the FAVAR literature is discussed. The methodology is used to study the effects
of monetary policy and news shocks.