This paper is concerned with linear dynamic factor models. In such models the observed process is decomposed into a structural part called the latent process, and a remainder that is called noise. The observed variables are treated in a symmetric way, so that no distinction between inputs and...

The ``REtrieval from MIxed Sampling'' (REMIS) approach based on blocking developed in Anderson et al. (2016) is concerned with retrieving an underlying high frequency model from mixed frequency observations. In this paper we investigate parameter-identifiability in the Johansen (1995) vector...

This article studies the sensitivity of Granger causality to the addition of noise, the introduction of subsampling, and the application of causal invertible filters to weakly stationary processes. Using canonical spectral factors and Wold decompositions, we give general conditions under which...

A survey is provided dealing with the formulation of modelling problems for dynamic factor models, and the various algorithm possibilities for solving these modelling problems. Emphasis is placed on understanding requirements for the handling of errors, noting the relevance of the proposed...

Factor Sequences are stochastic double sequences indexed in time and cross-section which have a so called factor structure. The name was coined by Forni and Lippi (2001) who introduced dynamic factor sequences. We show the difference between dynamic factor sequences and static factor sequences...