Process dynamic behaviors resulting from closed-loop control and the inherence of
processes are ubiquitous in industrial processes and bring a considerable challenge for …

Dynamic factor models are parsimonious representations of relationships among time series
variables. With the surge in data availability, they have proven to be indispensable in …

We propose the first comprehensive treatment of high-dimensional time series factor models
with multiple change-points in their second-order structure. We operate under the most …

The classical vector autoregressive model is a fundamental tool for multivariate time series
analysis. However, it involves too many parameters when the number of time series and lag …

Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM)
introduced in Forni et al.,(2000), have become extremely popular in the theory and practice …

In this article, which consists of two parts (Part I: representation results; Part II: estimation and
forecasting methods), we set up the theoretical foundations for a high‐dimensional …

Interconnectedness between stocks and firms plays a crucial role in the volatility contagion
phenomena that characterize financial crises, and graphs are a natural tool in their analysis …

Abstract High-Dimensional Dynamic Factor Models are presented in detail: The main
assumptions and their motivation, main results, illustrations by means of elementary …

We propose a new time-varying Generalized Dynamic Factor Model for high-dimensional,
locally stationary time series. Estimation is based on dynamic principal component analysis …

This article is the second one in a set of two laying the theoretical foundations for a high‐
dimensional functional factor model approach in the analysis of large cross‐sections …