Estimating and Applying Autoregression Models via Their Eigensystem Representation

Leo Krippner

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: This article introduces the eigensystem autoregression (EAR) framework, which allows an AR model to be specified, estimated, and applied directly in terms of its eigenvalues and eigenvectors. An EAR estimation can therefore impose various constraints on AR dynamics that would not be possible within standard linear estimation. Examples are restricting eigenvalue magnitudes to control the rate of mean reversion, additionally imposing that eigenvalues be real and positive to avoid pronounced oscillatory behavior, and eliminating the possibility of explosive episodes in a time-varying AR. The EAR framework also produces closed-form AR forecasts and associated variances, and forecasts and data may be decomposed into components associated with the AR eigenvalues to provide additional diagnostics for assessing the model.

Keywords: autoregression; lag polynomial; eigenvalues; eigenvectors; companion matrix (search for similar items in EconPapers)
JEL-codes: C22 C53 C63 (search for similar items in EconPapers)
Pages: 68 pages
Date: 2023-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Working Paper: Estimating and Applying Autoregression Models Via Their Eigensystem Representation (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2023-47

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