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Determination of cointegrating rank in partially non-stationary processes via a generalised von-Neumann criterion

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  • D. Harris
  • D. S. Poskitt

Abstract

In this paper we show that it is possible to characterise the cointegrating structure of a partially non-stationary, cointegrated, I(1) time series via the canonical correlations between the future and, the present and past, of the first differences of that series. This leads to a consideration of different model free non-parametric methodologies for identifying the cointegrating rank. An adaptation of existing techniques using a novel method of spectral estimation gives rise to both a new applied tool and an alternative analytical framework that unifies current hypothesis-testing approaches. An investigation of the eigenstructure of a multivariate version of von-Neumann's ratio also leads to the development of an entirely new model free cointegrating rank selection criterion. All the procedures considered are easily implemented and the practical relevance of the theoretical results obtained, which are founded on asymptotic arguments, is demonstrated by means of a simulation study. Copyright Royal Economic Socciety 2004

Suggested Citation

  • D. Harris & D. S. Poskitt, 2004. "Determination of cointegrating rank in partially non-stationary processes via a generalised von-Neumann criterion," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 191-217, June.
    • Handle: RePEc:ect:emjrnl:v:7:y:2004:i:1:p:191-217
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    Cited by:

    1. Abry, Patrice & Didier, Gustavo, 2018. "Wavelet eigenvalue regression for n-variate operator fractional Brownian motion," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 75-104.
    2. Zhang, Rongmao & Robinson, Peter & Yao, Qiwei, 2019. "Identifying cointegration by eigenanalysis," LSE Research Online Documents on Economics 87431, London School of Economics and Political Science, LSE Library.
    3. Karsten Reichold, 2022. "A Residuals-Based Nonparametric Variance Ratio Test for Cointegration," Papers 2211.06288, arXiv.org, revised Dec 2022.
    4. Ye Cai & Mototsugu Shintani, 2005. "On the Long-Run Variance Ratio Test for a Unit Root," Vanderbilt University Department of Economics Working Papers 0506, Vanderbilt University Department of Economics.
    5. Sella Lisa, 2008. "Old and New Spectral Techniques for Economic Time Series," Department of Economics and Statistics Cognetti de Martiis. Working Papers 200809, University of Turin.

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