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

Date

2012

Authors

Anderson, Brian
Deistler, Manfred
Felsenstein, Elisabeth
Funovits, Bernd
Zadrozny, Peter
Eichler, Michael
Chen, Weitien
Zamani, Mohsen

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

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.

Description

Keywords

Keywords: AR system; Frequency data; High frequency; Identifiability; Linear least-squares method; Multivariate autoregressive models; Noise parameters; Output variables; Second moments; Two ways; Control

Citation

Source

Proceedings of the IEEE Conference on Decision and Control

Type

Conference paper

Book Title

Entity type

Access Statement

License Rights

DOI

10.1109/CDC.2012.6426713

Restricted until

2037-12-31