Identifiability of regular and singular multivariate autoregressive models from mixed frequency data

Brian D.O. Anderson; Manfred Deistler; Elisabeth Felsenstein; Bernd Funovits; Peter Zadrozny; Michael Eichler; Weitian Chen; Mohsen Zamani

doi:10.1109/CDC.2012.6426713

Identifiability of regular and singular multivariate autoregressive models from mixed frequency data

Brian D.O. Anderson^*
, Manfred Deistler
, Elisabeth Felsenstein
, Bernd Funovits
, Peter Zadrozny
, Michael Eichler
, Weitian Chen
, Mohsen Zamani

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

14 Citations (Scopus)

Abstract

This paper is concerned with identifiability of an underlying high frequency multivariate AR system from mixed frequency observations. Such problems arise for instance in economics when some variables are observed monthly whereas others are observed quarterly. If we have identifiability, the system and noise parameters and thus all second moments of the output process can be estimated consistently from mixed frequency data. Then linear least squares methods for forecasting and interpolating nonobserved output variables can be applied. Two ways for guaranteeing generic identifiability are discussed.

Original language	English
Article number	6426713
Pages (from-to)	184-189
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
DOIs	https://doi.org/10.1109/CDC.2012.6426713
Publication status	Published - 2012
Event	51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States Duration: 10 Dec 2012 → 13 Dec 2012

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10.1109/CDC.2012.6426713

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title = "Identifiability of regular and singular multivariate autoregressive models from mixed frequency data",

abstract = "This paper is concerned with identifiability of an underlying high frequency multivariate AR system from mixed frequency observations. Such problems arise for instance in economics when some variables are observed monthly whereas others are observed quarterly. If we have identifiability, the system and noise parameters and thus all second moments of the output process can be estimated consistently from mixed frequency data. Then linear least squares methods for forecasting and interpolating nonobserved output variables can be applied. Two ways for guaranteeing generic identifiability are discussed.",

author = "Anderson, \{Brian D.O.\} and Manfred Deistler and Elisabeth Felsenstein and Bernd Funovits and Peter Zadrozny and Michael Eichler and Weitian Chen and Mohsen Zamani",

year = "2012",

doi = "10.1109/CDC.2012.6426713",

language = "English",

pages = "184--189",

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

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note = "51st IEEE Conference on Decision and Control, CDC 2012 ; Conference date: 10-12-2012 Through 13-12-2012",

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AU - Anderson, Brian D.O.

AU - Deistler, Manfred

AU - Felsenstein, Elisabeth

AU - Funovits, Bernd

AU - Zadrozny, Peter

AU - Eichler, Michael

AU - Chen, Weitian

AU - Zamani, Mohsen

PY - 2012

Y1 - 2012

N2 - This paper is concerned with identifiability of an underlying high frequency multivariate AR system from mixed frequency observations. Such problems arise for instance in economics when some variables are observed monthly whereas others are observed quarterly. If we have identifiability, the system and noise parameters and thus all second moments of the output process can be estimated consistently from mixed frequency data. Then linear least squares methods for forecasting and interpolating nonobserved output variables can be applied. Two ways for guaranteeing generic identifiability are discussed.

AB - This paper is concerned with identifiability of an underlying high frequency multivariate AR system from mixed frequency observations. Such problems arise for instance in economics when some variables are observed monthly whereas others are observed quarterly. If we have identifiability, the system and noise parameters and thus all second moments of the output process can be estimated consistently from mixed frequency data. Then linear least squares methods for forecasting and interpolating nonobserved output variables can be applied. Two ways for guaranteeing generic identifiability are discussed.

UR - https://www.scopus.com/pages/publications/84874222474

U2 - 10.1109/CDC.2012.6426713

DO - 10.1109/CDC.2012.6426713

M3 - Conference article

AN - SCOPUS:84874222474

SN - 0743-1546

SP - 184

EP - 189

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

M1 - 6426713

T2 - 51st IEEE Conference on Decision and Control, CDC 2012

Y2 - 10 December 2012 through 13 December 2012

ER -

Identifiability of regular and singular multivariate autoregressive models from mixed frequency data

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