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M. Deistler, Brian Anderson, E. Felsenstein, B. Funovits, L. Kölbl, M. Zamani:
"Multivariate AR Systems and Mixed Frequency Data: g-Identifiability and Estimation";
Talk: Cfe Ercim 2014, Pisa; 2014-12-06 - 2014-12-08.

The research is concerned with the problem of identifiability of the parameters of a high frequency multivariate autoregressive model from mixed
frequency time series data. We demonstrate identifiability for generic parameter values using the population second moments corresponding to the
observations. In addition we display a constructive algorithm for the parameter values and establish the continuity of the mapping attaching the
high frequency parameters to these population second moments. These structural results are obtained using two alternative tools viz. extended Yule
Walker equations and blocking of the the output process. The cases of stock and how variables as well as of general linear transformations of high
frequency data are treated. Finally, we shortly discuss how our constructive identifiability results can be used for parameter estimation based on the
sample second moments