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Estimating and Applying Autoregression Models Via Their Eigensystem Representation

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  • Leo Krippner

    (University of Waikato)

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.

Suggested Citation

  • Leo Krippner, 2023. "Estimating and Applying Autoregression Models Via Their Eigensystem Representation," Working Papers in Economics 23/09, University of Waikato.
    • Handle: RePEc:wai:econwp:23/09
    as

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    File URL: https://repec.its.waikato.ac.nz/wai/econwp/2309.pdf
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    References listed on IDEAS

    as
    1. Georgi N. Boshnakov & Bisher M. Iqelan, 2009. "Generation Of Time Series Models With Given Spectral Properties," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(3), pages 349-368, May.
    2. Phillips, Peter C.B. & Magdalinos, Tassos, 2007. "Limit theory for moderate deviations from a unit root," Journal of Econometrics, Elsevier, vol. 136(1), pages 115-130, January.
    3. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    4. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
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    More about this item

    Keywords

    autoregression; lag polynomial; eigenvalues; eigenvectors; companion matrix;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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