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Testing for Multivariate Autocorrelation

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  • H. E. T. Holgersson

Abstract

This paper concerns the problem of assessing autocorrelation of multivariate (i.e. systemwise) models. It is well known that systemwise diagnostic tests for autocorrelation often suffers from poor small sample properties in the sense that the true size overstates the nominal size. The failure of keeping control of the size usually stems from the fact that the critical values (used to decide the rejection area) originate from the slowly converging asymptotic null distribution. Another drawback of existing tests is that the power may be rather low if the deviation from the null is not symmetrical over the marginal models. In this paper we consider four quite different test techniques for autocorrelation. These are (i) Pillai's trace, (ii) Roy's largest root, (iii) the maximum F-statistic and (iv) the maximum t2 test. We show how to obtain control of the size of the tests, and then examine the true (small sample) size and power properties by means of Monte Carlo simulations.

Suggested Citation

  • H. E. T. Holgersson, 2004. "Testing for Multivariate Autocorrelation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(4), pages 379-395.
    • Handle: RePEc:taf:japsta:v:31:y:2004:i:4:p:379-395
    DOI: 10.1080/02664760410001681693
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    Cited by:

    1. Andersson, Eva, 2007. "Effect of dependency in systems for multivariate surveillance," Research Reports 2007:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.

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