Multivariate AR Systems and Mixed Frequency Data: G-Identifiability and Estimation

Brian D. O. Anderson, Manfred Deistler, Elisabeth Felsenstein, Bernd Funovits, Lukas Koelbl, Mohsen Zamani

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review


This paper 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 of 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 output process. The cases of stock and flow variables,
as well as of general linear transformations of high frequency data, are treated. Finally, we briefly discuss how our constructive identifiability results can be used for
parameter estimation based on the sample second moments.
TidskriftEconometric Theory
Sidor (från-till)793-826
Antal sidor34
StatusPublicerad - aug 2016
Externt publiceradJa
MoE-publikationstypA1 Tidskriftsartikel-refererad


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