High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research

Marco Lippi; Manfred Deistler; Brian Anderson

doi:10.1016/j.ecosta.2022.03.008

High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research

Marco Lippi, Manfred Deistler^*, Brian Anderson

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

18 Citations (Scopus)

Abstract

High-Dimensional Dynamic Factor Models are presented in detail: The main assumptions and their motivation, main results, illustrations by means of elementary examples. In particular, the role of singular ARMA models in the theory and applications of High-Dimensional Dynamic Factor Models is discussed. The emphasis is on model classes and their structure theory, rather than on estimation in the narrow sense. The survey is not comprehensive. Its aim is to point out promising lines of research and applications that have not yet been sufficiently developed.

Original language	English
Pages (from-to)	3-16
Number of pages	14
Journal	Econometrics and Statistics
Volume	26
DOIs	https://doi.org/10.1016/j.ecosta.2022.03.008
Publication status	Published - Apr 2023

Access to Document

10.1016/j.ecosta.2022.03.008

Cite this

@article{b1ada6f0024d412699715a33989da576,

title = "High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research",

abstract = "High-Dimensional Dynamic Factor Models are presented in detail: The main assumptions and their motivation, main results, illustrations by means of elementary examples. In particular, the role of singular ARMA models in the theory and applications of High-Dimensional Dynamic Factor Models is discussed. The emphasis is on model classes and their structure theory, rather than on estimation in the narrow sense. The survey is not comprehensive. Its aim is to point out promising lines of research and applications that have not yet been sufficiently developed.",

keywords = "Dynamic factor models, High-dimensional vector processes, Singular ARMA vector processes, State-space representations",

author = "Marco Lippi and Manfred Deistler and Brian Anderson",

note = "Publisher Copyright: {\textcopyright} 2022 The Authors",

year = "2023",

month = apr,

doi = "10.1016/j.ecosta.2022.03.008",

language = "English",

volume = "26",

pages = "3--16",

journal = "Econometrics and Statistics",

issn = "2452-3062",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - High-Dimensional Dynamic Factor Models

T2 - A Selective Survey and Lines of Future Research

AU - Lippi, Marco

AU - Deistler, Manfred

AU - Anderson, Brian

PY - 2023/4

Y1 - 2023/4

N2 - High-Dimensional Dynamic Factor Models are presented in detail: The main assumptions and their motivation, main results, illustrations by means of elementary examples. In particular, the role of singular ARMA models in the theory and applications of High-Dimensional Dynamic Factor Models is discussed. The emphasis is on model classes and their structure theory, rather than on estimation in the narrow sense. The survey is not comprehensive. Its aim is to point out promising lines of research and applications that have not yet been sufficiently developed.

AB - High-Dimensional Dynamic Factor Models are presented in detail: The main assumptions and their motivation, main results, illustrations by means of elementary examples. In particular, the role of singular ARMA models in the theory and applications of High-Dimensional Dynamic Factor Models is discussed. The emphasis is on model classes and their structure theory, rather than on estimation in the narrow sense. The survey is not comprehensive. Its aim is to point out promising lines of research and applications that have not yet been sufficiently developed.

KW - Dynamic factor models

KW - High-dimensional vector processes

KW - Singular ARMA vector processes

KW - State-space representations

UR - https://www.scopus.com/pages/publications/85128851175

U2 - 10.1016/j.ecosta.2022.03.008

DO - 10.1016/j.ecosta.2022.03.008

M3 - Article

SN - 2452-3062

VL - 26

SP - 3

EP - 16

JO - Econometrics and Statistics

JF - Econometrics and Statistics

ER -

High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research

Abstract

Access to Document

Other files and links

Fingerprint

Cite this