Scientific Data Services

“Using Subspace Methods for the Estimation of Approximate Dynamic Factor Models” is the title of Dietmar Bauers's lecture, which is being organised by the "Zentrum für Statistik" at Bielefeld University. It will take place on Tuesday, 15 April 2025 from 12:00 to 13:00 in W9-109.

For multivariate time series with a large number of variables classical vector autoregressive (VAR) models are not appropriate because they contain too many parameters. Alternatively in the literature in such situations factor models are used to reduce the dimensionality. Approximate dynamic factor models represent the high-dimensional time series as generated by a common factor part and idiosyncratic terms, where the common factors are latent. Estimating the dynamics of the common factors often is done using a VAR model for the principal components. The estimation of more flexible state space models via maximum likelihood methods is more complicated. Subspace methods are a numerically stable alternative that can be used in this respect. In this talk we show that the subspace methods provide a very robust and computationally simple means to obtain consistent estimators for the latent factor dynamics.