Extended Yule-Walker identification of Varma models with single- or mixed frequency data

Peter Zadrozny

No 526, CFS Working Paper Series from Center for Financial Studies (CFS)

Abstract: Chen and Zadrozny (1998) developed the linear extended Yule-Walker (XYW) method for determining the parameters of a vector autoregressive (VAR) model with available covariances of mixed-frequency observations on the variables of the model. If the parameters are determined uniquely for available population covariances, then, the VAR model is identified. The present paper extends the original XYW method to an extended XYW method for determining all ARMA parameters of a vector autoregressive moving-average (VARMA) model with available covariances of single- or mixed-frequency observations on the variables of the model. The paper proves that under conditions of stationarity, regularity, miniphaseness, controllability, observability, and diagonalizability on the parameters of the model, the parameters are determined uniquely with available population covariances of single- or mixed-frequency observations on the variables of the model, so that the VARMA model is identified with the single- or mixed-frequency covariances.

Keywords: block-Vandermonde eigenvectors of block-companion state-transition; matrix of state-space representation; matrix spectral factorization (search for similar items in EconPapers)
JEL-codes: C32 C80 (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-ecm and nep-ets
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Related works:
Journal Article: Extended Yule–Walker identification of VARMA models with single- or mixed-frequency data (2016)
Working Paper: Extended Yule-Walker Identification of Varma Models with Single- or Mixed-Frequency Data (2016)
Working Paper: Extended Yule-Walker Identification of Varma Models with Single- or Mixed- Frequency Data (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:526

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