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On The Estimation Of The Inverse Correlation Function

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  • Francesco Battaglia

Abstract

Two new methods for estimating the inverse covariance and inverse correlation functions of a time series are proposed. One of them is based on an orthogonality property, the other is suggested by interpolation considerations. The two methods are shown to be asymptotically equivalent, and their asymptotic distribution is derived. The asymptotic distribution turns out to be the same as that of the autoregressive estimates of the inverse correlations. The problem of choosing an estimation method in practice is discussed.

Suggested Citation

  • Francesco Battaglia, 1988. "On The Estimation Of The Inverse Correlation Function," Journal of Time Series Analysis, Wiley Blackwell, vol. 9(1), pages 1-10, January.
    • Handle: RePEc:bla:jtsera:v:9:y:1988:i:1:p:1-10
    DOI: 10.1111/j.1467-9892.1988.tb00448.x
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    Cited by:

    1. R. H. Glendinning, 2000. "Estimating the Inverse Autocorrelation Function from Outlier Contaminated Data," Computational Statistics, Springer, vol. 15(4), pages 541-565, December.
    2. Caiado, Jorge & Crato, Nuno & Pena, Daniel, 2006. "A periodogram-based metric for time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2668-2684, June.
    3. Proietti, Tommaso & Luati, Alessandra, 2015. "The generalised autocovariance function," Journal of Econometrics, Elsevier, vol. 186(1), pages 245-257.
    4. Roberto Baragona & Francesco Battaglia, 1995. "Linear Interpolators And The Inverse Correlation Function Of Non‐Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(6), pages 531-538, November.
    5. Alessandra Luati & Francesca Papagni & Tommaso Proietti, 2021. "Efficient Nonparametric Estimation of Generalized Autocovariances," CEIS Research Paper 515, Tor Vergata University, CEIS, revised 14 Oct 2021.
    6. Baragona, Roberto & Battaglia, Francesco & Calzini, Claudio, 2001. "Genetic algorithms for the identification of additive and innovation outliers in time series," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 1-12, July.

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