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Asymptotic Relative Efficiency of Goodness-Of-Fit Tests Based on Inverse and Ordinary Autocorrelations

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  • Ahmed El Ghini
  • Christian Francq

Abstract

We compare the performance of the inverse and ordinary (partial) autocorrelations for time series model identification. It is found that, both in terms of Bahadur's slope and Pitman's asymptotic relative efficiency, the inverse partial autocorrelations are more efficient than the ordinary autocorrelations for identification of moving-average models. By duality, the partial autocorrelations turn out to be more powerful than the inverse autocorrelations to identify autoregressive models. Numerical experiments on both simulated and real data sets are presented to highlight the theoretical results. Copyright 2006 The Authors Journal compilation 2006 Blackwell Publishing Ltd.

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  • Ahmed El Ghini & Christian Francq, 2006. "Asymptotic Relative Efficiency of Goodness-Of-Fit Tests Based on Inverse and Ordinary Autocorrelations," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(6), pages 843-855, November.
  • Handle: RePEc:bla:jtsera:v:27:y:2006:i:6:p:843-855
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    Cited by:

    1. Proietti, Tommaso & Luati, Alessandra, 2015. "The generalised autocovariance function," Journal of Econometrics, Elsevier, vol. 186(1), pages 245-257.

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