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Vector autoregressive moving average identification for macroeconomic modeling: A new methodology

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  • Poskitt, D.S.

Abstract

This paper develops a new methodology for identifying the structure of VARMA time series models. The analysis proceeds by examining the echelon form and presents a fully automatic, strongly consistent, data driven approach to model specification. A novel feature of the inferential procedures developed here is that they work in terms of a canonical representation based on the Kronecker invariants in which the variables are expressed in the form of scalar dynamic structural equations derived from the VARMA system. This feature facilitates the construction of procedures which, from the perspective of macroeconomic modeling, can be efficacious in that they do not rely on VAR approximations. Techniques that are applicable to both asymptotically stationary and unit-root, partially nonstationary (cointegrated) time series models are presented. The inferential potential of the techniques is illustrated via simulation experiments that use data generating mechanisms based on real world examples drawn from the time series literature. Aspects of the Kronecker invariants that impinge on practical application and that have not hitherto been discussed in the literature are explored.

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  • Poskitt, D.S., 2016. "Vector autoregressive moving average identification for macroeconomic modeling: A new methodology," Journal of Econometrics, Elsevier, vol. 192(2), pages 468-484.
    • Handle: RePEc:eee:econom:v:192:y:2016:i:2:p:468-484
    DOI: 10.1016/j.jeconom.2016.02.011
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    2. Bernd Funovits, 2020. "Identifiability and Estimation of Possibly Non-Invertible SVARMA Models: A New Parametrisation," Papers 2002.04346, arXiv.org, revised Feb 2021.
    3. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2017. "Inference for impulse response coefficients from multivariate fractionally integrated processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 60-84, March.
    4. Bernd Funovits, 2019. "Identification and Estimation of SVARMA models with Independent and Non-Gaussian Inputs," Papers 1910.04087, arXiv.org.
    5. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

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