  We propose a new parametrization for the estimation and identification of the
impulse-response functions (IRFs) of dynamic factor models (DFMs). The
theoretical contribution of this paper concerns the problem of observational
equivalence between different IRFs, which implies non-identification of the IRF
parameters without further restrictions. We show how the previously proposed
minimal identification conditions are nested in the new framework and can be
further augmented with overidentifying restrictions leading to efficiency
gains. The current standard practice for the IRF estimation of DFMs is based on
principal components, compared to which the new parametrization is less
restrictive and allows for modelling richer dynamics. As the empirical
contribution of the paper, we develop an estimation method based on the EM
algorithm, which incorporates the proposed identification restrictions. In the
empirical application, we use a standard high-dimensional macroeconomic dataset
to estimate the effects of a monetary policy shock. We estimate a strong
reaction of the macroeconomic variables, while the benchmark models appear to
give qualitatively counterintuitive results. The estimation methods are
implemented in the accompanying R package.
