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The Co-Integrated Vector Autoregression with Errors-in-Variables

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  • Heino Bohn Nielsen
Abstract
The co-integrated vector autoregression is extended to allow variables to be observed with classical measurement errors (ME). For estimation, the model is parametrized as a time invariant state-space form, and an accelerated expectation-maximization algorithm is derived. A simulation study shows that (i) the finite-sample properties of the maximum likelihood (ML) estimates and reduced rank test statistics are excellent (ii) neglected measurement errors will generally distort unit root inference due to a moving average component in the residuals, and (iii) the moving average component may-in principle-be approximated by a long autoregression, but a pure autoregression cannot identify the autoregressive structure of the latent process, and the adjustment coefficients are estimated with a substantial asymptotic bias. An application to the zero-coupon yield-curve is given.

Suggested Citation

  • Heino Bohn Nielsen, 2016. "The Co-Integrated Vector Autoregression with Errors-in-Variables," Econometric Reviews, Taylor & Francis Journals, vol. 35(2), pages 169-200, February.
    • Handle: RePEc:taf:emetrv:v:35:y:2016:i:2:p:169-200
    DOI: 10.1080/07474938.2013.806853
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    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
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    Cited by:

    1. Andreas Hetland, 2018. "The Stochastic Stationary Root Model," Econometrics, MDPI, vol. 6(3), pages 1-33, August.

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