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Inference for Autocorrelations in the Possible Presence of a Unit Root

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  • Dimitris N. Politis
  • Joseph P. Romano
  • Michael Wolf

Abstract

We consider the problem of making inference for the autocorrelations of a time series in the possible presence of a unit root. Even when the underlying series is assumed to be strictly stationary, the robustness against a unit root is a desirable property to ensure good finite-sample coverage in case the series has a near unit root. In addition to discussing a confidence interval for the autocorrelation at a given lag, we also consider a simultaneous confidence band for the first k autocorrelations. We suggest the use of the subsampling method applied to properly studentized statistics, which results in confidence intervals and bands with asymptotically correct coverage probability. An application to practical model selection is given, while a simulation study examines finite-sample performance. Copyright 2004 Blackwell Publishing Ltd.

Suggested Citation

  • Dimitris N. Politis & Joseph P. Romano & Michael Wolf, 2004. "Inference for Autocorrelations in the Possible Presence of a Unit Root," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 251-263, March.
  • Handle: RePEc:bla:jtsera:v:25:y:2004:i:2:p:251-263
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    Cited by:

    1. Le Pen, Yannick, 2011. "A pair-wise approach to output convergence between European regions," Economic Modelling, Elsevier, vol. 28(3), pages 955-964, May.
    2. Giacomini, Raffaella & Politis, Dimitris N. & White, Halbert, 2013. "A Warp-Speed Method For Conducting Monte Carlo Experiments Involving Bootstrap Estimators," Econometric Theory, Cambridge University Press, vol. 29(03), pages 567-589, June.
    3. Caggiano, Giovanni & Leonida, Leone, 2007. "A note on the empirics of the neoclassical growth model," Economics Letters, Elsevier, vol. 94(2), pages 170-176, February.
    4. Giovanni Caggiano & Leone Leonida, 2009. "International output convergence: evidence from an autocorrelation function approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 139-162.

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