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

Anderson, Brian D.O.; Deistler, Manfred; Felsenstein, Elisabeth; Funovits, Bernd; Koelbl, Lukas and Zamani, Mohsen (2016) Multivariate AR Systems and mixed Frequency Data: G-Identifiability and Estimation. Econometric Theory, 32 (4), pp. 793-826.

Full text not available from this repository.


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. (author's abstract)

Item Type: Article in Academic Journal
Research Units: Former Research Groups (until 2017) > Macroeconomics and Public Finance
Date Deposited: 22 Jun 2017 07:21
Last Modified: 22 Jun 2017 07:22
DOI: 10.1017/S0266466615000043
ISSN: 0266-4666

Actions (login required)

View Item View Item