This article surveys work on a class of models, dynamic factor models (DFMs), that has
received considerable attention in the past decade because of their ability to model …

This paper is concerned with data science and analytics as applied to data from dynamic
systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in …

Producing probabilistic forecasts for large collections of similar and/or dependent time series
is a practically highly relevant, yet challenging task. Classical time series models fail to …

This paper deals with the factor modeling for high-dimensional time series based on a
dimension-reduction viewpoint. Under stationary settings, the inference is simple in the …

The procedure proposed by Bai and Ng (2002) for identifying the number of factors in static
factor models is revisited. In order to improve its performance, we introduce a tuning …

Supplement to “Network vector autoregression”. The supplementary material [35] contains
the verification of (2.6) and (2.7), proofs of Theorem 1, Theorem 4, Theorem 5, two useful …

Large tensor (multi-dimensional array) data routinely appear nowadays in a wide range of
applications, due to modern data collection capabilities. Often such observations are taken …

This paper deals with the dimension reduction of high-dimensional time series based on a
lower-dimensional factor process. In particular, we allow the dimension of time series N to …

In finance, economics and many other fields, observations in a matrix form are often
observed over time. For example, many economic indicators are obtained in different …

Determining the number of common factors is an important and practical topic in high-
dimensional factor models. The existing literature is mainly based on the eigenvalues of the …