Singular autoregressions for generalized dynamic factor models

Manfred Deistler; Alexander Filler; Brian D.O. Anderson; Weitian Chen; Elisabeth Felsenstein

doi:10.1109/CDC.2010.5718025

Singular autoregressions for generalized dynamic factor models

Manfred Deistler^*, Alexander Filler, Brian D.O. Anderson, Weitian Chen, Elisabeth Felsenstein

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

We consider Generalized Linear Dynamic Factor Models in a stationary context, where the latent variables and thus the static and dynamic factors are the sum of a linearly regular and a linearly singular stationary process and the noise process is linearly regular. The linearly singular component may be useful for modeling e.g. business cycles or seasonal fluctuations in the observed variables. We present a structure theory for this case. The emphasis is laid on the autoregressive case. In general the stationary solutions of the autoregressive models considered here consist of a linearly regular and a linearly singular part. The linearly singular part corresponds to the homogeneous solution of a system having stable roots as well as roots of modulus one. We discuss the solutions of the Yule Walker equations for this case.

Original language	English
Title of host publication	2010 49th IEEE Conference on Decision and Control, CDC 2010
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2875-2879
Number of pages	5
ISBN (Print)	9781424477456
DOIs	https://doi.org/10.1109/CDC.2010.5718025
Publication status	Published - 2010
Event	49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, United States Duration: 15 Dec 2010 → 17 Dec 2010

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	49th IEEE Conference on Decision and Control, CDC 2010
Country/Territory	United States
City	Atlanta
Period	15/12/10 → 17/12/10

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10.1109/CDC.2010.5718025

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Deistler, M., Filler, A., Anderson, B. D. O., Chen, W., & Felsenstein, E. (2010). Singular autoregressions for generalized dynamic factor models. In 2010 49th IEEE Conference on Decision and Control, CDC 2010 (pp. 2875-2879). Article 5718025 (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2010.5718025

@inproceedings{e76aee1d658a43cdbfbc4a52db02db00,

title = "Singular autoregressions for generalized dynamic factor models",

abstract = "We consider Generalized Linear Dynamic Factor Models in a stationary context, where the latent variables and thus the static and dynamic factors are the sum of a linearly regular and a linearly singular stationary process and the noise process is linearly regular. The linearly singular component may be useful for modeling e.g. business cycles or seasonal fluctuations in the observed variables. We present a structure theory for this case. The emphasis is laid on the autoregressive case. In general the stationary solutions of the autoregressive models considered here consist of a linearly regular and a linearly singular part. The linearly singular part corresponds to the homogeneous solution of a system having stable roots as well as roots of modulus one. We discuss the solutions of the Yule Walker equations for this case.",

keywords = "Generalized dynamic factor models, High dimensional time series, Identification, Linearly regular and linearly singular stationary processes, Yule walker equations",

author = "Manfred Deistler and Alexander Filler and Anderson, {Brian D.O.} and Weitian Chen and Elisabeth Felsenstein",

year = "2010",

doi = "10.1109/CDC.2010.5718025",

language = "English",

isbn = "9781424477456",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2875--2879",

booktitle = "2010 49th IEEE Conference on Decision and Control, CDC 2010",

address = "United States",

note = "49th IEEE Conference on Decision and Control, CDC 2010 ; Conference date: 15-12-2010 Through 17-12-2010",

}

Deistler, M, Filler, A, Anderson, BDO, Chen, W & Felsenstein, E 2010, Singular autoregressions for generalized dynamic factor models. in 2010 49th IEEE Conference on Decision and Control, CDC 2010., 5718025, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 2875-2879, 49th IEEE Conference on Decision and Control, CDC 2010, Atlanta, United States, 15/12/10. https://doi.org/10.1109/CDC.2010.5718025

Singular autoregressions for generalized dynamic factor models. / Deistler, Manfred; Filler, Alexander; Anderson, Brian D.O. et al.
2010 49th IEEE Conference on Decision and Control, CDC 2010. Institute of Electrical and Electronics Engineers Inc., 2010. p. 2875-2879 5718025 (Proceedings of the IEEE Conference on Decision and Control).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Singular autoregressions for generalized dynamic factor models

AU - Deistler, Manfred

AU - Filler, Alexander

AU - Anderson, Brian D.O.

AU - Chen, Weitian

AU - Felsenstein, Elisabeth

PY - 2010

Y1 - 2010

N2 - We consider Generalized Linear Dynamic Factor Models in a stationary context, where the latent variables and thus the static and dynamic factors are the sum of a linearly regular and a linearly singular stationary process and the noise process is linearly regular. The linearly singular component may be useful for modeling e.g. business cycles or seasonal fluctuations in the observed variables. We present a structure theory for this case. The emphasis is laid on the autoregressive case. In general the stationary solutions of the autoregressive models considered here consist of a linearly regular and a linearly singular part. The linearly singular part corresponds to the homogeneous solution of a system having stable roots as well as roots of modulus one. We discuss the solutions of the Yule Walker equations for this case.

AB - We consider Generalized Linear Dynamic Factor Models in a stationary context, where the latent variables and thus the static and dynamic factors are the sum of a linearly regular and a linearly singular stationary process and the noise process is linearly regular. The linearly singular component may be useful for modeling e.g. business cycles or seasonal fluctuations in the observed variables. We present a structure theory for this case. The emphasis is laid on the autoregressive case. In general the stationary solutions of the autoregressive models considered here consist of a linearly regular and a linearly singular part. The linearly singular part corresponds to the homogeneous solution of a system having stable roots as well as roots of modulus one. We discuss the solutions of the Yule Walker equations for this case.

KW - Generalized dynamic factor models

KW - High dimensional time series

KW - Identification

KW - Linearly regular and linearly singular stationary processes

KW - Yule walker equations

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U2 - 10.1109/CDC.2010.5718025

DO - 10.1109/CDC.2010.5718025

M3 - Conference contribution

SN - 9781424477456

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 2875

EP - 2879

BT - 2010 49th IEEE Conference on Decision and Control, CDC 2010

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 49th IEEE Conference on Decision and Control, CDC 2010

Y2 - 15 December 2010 through 17 December 2010

ER -

Singular autoregressions for generalized dynamic factor models

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