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

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

Forskningsoutput: TidskriftsbidragKonferensartikelVetenskapligPeer review


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.
TidskriftProceedings of the IEEE Conference on Decision & Control
Sidor (från-till)184-189
Antal sidor6
StatusPublicerad - 2012
Externt publiceradJa
MoE-publikationstypA4 Artikel i en konferenspublikation
Evenemang51st IEEE Conference on Decision and Control - Maui, Förenta Staterna (USA)
Varaktighet: 10 dec 201213 dec 2012
Konferensnummer: 51


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