TY - JOUR
T1 - Identifiability of regular and singular multivariate autoregressive models from mixed frequency data
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 - http://www.scopus.com/inward/record.url?scp=84874222474&partnerID=8YFLogxK
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 -