We develop Granger causality tests that apply directly to data sampled at different
frequencies. We show that taking advantage of mixed frequency data allows us to better …

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 …

Causal inference in multivariate time series is challenging because the sampling rate may
not be as fast as the time scale of the causal interactions, so the observed series is a …

We examine the effects of mixed sampling frequencies and temporal aggregation on the
size of commonly used tests for cointegration, and we find that these effects may be severe …

This paper is concerned with the structure of multivariate AR and ARMA systems. The
emphasis is on two “non-standard” cases: We deal with the structure of singular AR and …

Learning the dependency structure between spatially distributed observations of a spatio-
temporal process is an important problem in many fields such as geology, geophysics …

Chen and Zadrozny (1998) developed the linear extended Yule–Walker (XYW) method for
determining the parameters of a vector autoregressive (VAR) model with available …

In this contribution we present a structure theory for generalized linear dynamic factor
models. Generalized dynamic factor models have been proposed approximately a decade …

Recently, identifiability results for VAR systems in the context of mixed frequency data have
been shown in a number of papers. These results have been extended to VARMA systems …

This paper is concerned with estimation of the parameters of a high-frequency VAR model
using mixed-frequency data, both for the stock and for the flow case. Extended Yule–Walker …