MULTIVARIATE AR SYSTEMS AND MIXED FREQUENCY DATA: G-IDENTIFIABILITY AND ESTIMATION

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...

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Published inEconometric theory Vol. 32; no. 4; pp. 793 - 826
Main Authors Anderson, Brian D.O., Deistler, Manfred, Felsenstein, Elisabeth, Funovits, Bernd, Koelbl, Lukas, Zamani, Mohsen
Format Journal Article
LanguageEnglish
Published New York, USA Cambridge University Press 01.08.2016
Subjects
Algorithms
Econometrics
ET LECTURE
Parameter estimation
Population
Studies
System theory
Time series
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Summary: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.
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ISSN:0266-4666
1469-4360
DOI:10.1017/S0266466615000043