3 papers
stat.ME2022
Sparse Dynamic Factor Models with Loading Selection by Variational Inference
Erik Spånberg
In this paper we develop a novel approach for estimating large and sparse dynamic factor models using variational inference, also allowing for missing data. Inspired by Bayesian va…
stat.ME2022
Variational Inference of Dynamic Factor Models with Arbitrary Missing Data
Erik Spånberg
Dynamic factor models are often estimated by point-estimation methods, disregarding parameter uncertainty. We propose a method accounting for parameter uncertainty by means of post…
stat.ME2022
Maximum a Posteriori Estimation of Dynamic Factor Models with Incomplete Data
Erik Spånberg
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 maximiza…