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We propose a new parametrization for the estimation and identification of theimpulse-response functions (IRFs) of dynamic factor models (DFMs). Thetheoretical contribution of this paper concerns the problem of observationalequivalence between different IRFs, which implies non-identification of the IRFparameters without further restrictions. We show how the previously proposedminimal identification conditions are nested in the new framework and can befurther augmented with overidentifying restrictions leading to efficiencygains. The current standard practice for the IRF estimation of DFMs is based onprincipal components, compared to which the new parametrization is lessrestrictive and allows for modelling richer dynamics. As the empiricalcontribution of the paper, we develop an estimation method based on the EMalgorithm, which incorporates the proposed identification restrictions. In theempirical application, we use a standard high-dimensional macroeconomic datasetto estimate the effects of a monetary policy shock. We estimate a strongreaction of the macroeconomic variables, while the benchmark models appear togive qualitatively counterintuitive results. The estimation methods areimplemented in the accompanying R package.

Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization