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Locally optimal tests against periodic autoregression: parametric and nonparametric approaches

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  • Mohamed Bentarzi
  • Marc Hallin

Abstract

Locally asymptotically optimal tests are derived for the null hypothesis of traditional AR dependence, with unspecified AR coefficients and unspecified innovation densities, against an alternative of periodically correlated AR dependence. Parametric and nonparametric rank-based versions are proposed. Local powers and asymptotic relative efficiencies (with respect, e.g. to the corresponding Gaussian Lagrange multiplier tests proposed in Ghysels and Hall [1992, "Lagrange Multiplier Tests for Periodic Structures," unpublished manuscript, CRDE, Montreal] and Lutkepohl [1991, Introduction to Multiple Time Series Analysis, Berlin: Springer-Verlag; 1991, pp. 243-264, in W.E. Griffiths, H. Lütkepohl, & M.E. Block (eds.), Readings in Econometric Theory and Practice, Amsterdam: North-Holland] are computed explicitly; a rank-based test of the van der Waerden type is proposed, for which this ARE is uniformly larger than 1. The main technical tool is Le Cam's local asymptotic normality property. © 1996 Cambridge University Press.

Suggested Citation

  • Mohamed Bentarzi & Marc Hallin, 1996. "Locally optimal tests against periodic autoregression: parametric and nonparametric approaches," ULB Institutional Repository 2013/2063, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:ulb:ulbeco:2013/2063
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    Cited by:

    1. Bentarzi, Mohamed, 1998. "Model-Building Problem of Periodically Correlatedm-Variate Moving Average Processes," Journal of Multivariate Analysis, Elsevier, vol. 66(1), pages 1-21, July.

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