[Zurück]

Brian Anderson, M. Deistler, E. Felsenstein, B. Funovits, P. Zadrozny, M. Eichler, W. Chen, M. Zamani:
"Identifiability of regular and singular multivariate autoregressive models from mixed frequency data, Part 2";
Vortrag: 1st Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance 2013 (Time Series 2013), Wien; 02.05.2013 - 04.05.2013.

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.

http://www.ihs.ac.at/conferences/timeseries/files/Time_Series_2013_Scientific_Program_record.pdf