  In this paper, we present a method of maximum a posteriori estimation of
parameters in dynamic factor models with incomplete data. We extend maximum
likelihood expectation maximization iterations by Ba\'nbura & Modugno (2014) to
penalized counterparts by applying parameter shrinkage in a Minnesota prior
style fashion, also considering factors loading onto variables dynamically. A
heuristic and adapting shrinkage scheme is considered. The algorithm is
applicable to any arbitrary pattern of missing data, including different
publication dates, sample lengths and frequencies. The method is evaluated in a
Monte Carlo study, generally performing favourably, and at least comparably, to
maximum likelihood.
