  Macroeconomic forecasting has recently started embracing techniques that can
deal with large-scale datasets and series with unequal release periods.
MIxed-DAta Sampling (MIDAS) and Dynamic Factor Models (DFM) are the two main
state-of-the-art approaches that allow modeling series with non-homogeneous
frequencies. We introduce a new framework called the Multi-Frequency Echo State
Network (MFESN) based on a relatively novel machine learning paradigm called
reservoir computing. Echo State Networks (ESN) are recurrent neural networks
formulated as nonlinear state-space systems with random state coefficients
where only the observation map is subject to estimation. MFESNs are
considerably more efficient than DFMs and allow for incorporating many series,
as opposed to MIDAS models, which are prone to the curse of dimensionality. All
methods are compared in extensive multistep forecasting exercises targeting US
GDP growth. We find that our MFESN models achieve superior or comparable
performance over MIDAS and DFMs at a much lower computational cost.
