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More limit theory for the sample correlation function of moving averages

Author

Listed:
  • Davis, Richard
  • Resnick, Sidney

Abstract

Let Xt = [Sigma][infinity]j=-[infinity] cjZt - j be a moving average process where {Zt} is iid with common distribution in the domain of attraction of a stable law with index [alpha], 0

Suggested Citation

  • Davis, Richard & Resnick, Sidney, 1985. "More limit theory for the sample correlation function of moving averages," Stochastic Processes and their Applications, Elsevier, vol. 20(2), pages 257-279, September.
    • Handle: RePEc:eee:spapps:v:20:y:1985:i:2:p:257-279
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    Citations

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    Cited by:

    1. W. K. Li & Shiqing Ling & Michael McAleer, 2001. "A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors," ISER Discussion Paper 0545, Institute of Social and Economic Research, Osaka University.
    2. Jonathan B. Hill, 2004. "Strong Orthogonal Decompositions and Non-Linear Impulse Response Functions for Infinite Variance Processes," Econometrics 0401001, University Library of Munich, Germany, revised 16 Dec 2005.
    3. Phillips, Peter C. B. & Loretan, Mico, 1991. "The Durbin-Watson ratio under infinite-variance errors," Journal of Econometrics, Elsevier, vol. 47(1), pages 85-114, January.
    4. Bouhaddioui, Chafik & Ghoudi, Kilani, 2012. "Empirical processes for infinite variance autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 319-335.
    5. Andrews, Beth & Davis, Richard A., 2013. "Model identification for infinite variance autoregressive processes," Journal of Econometrics, Elsevier, vol. 172(2), pages 222-234.
    6. Yuya Sasaki & Yulong Wang, 2020. "Testing Finite Moment Conditions for the Consistency and the Root-N Asymptotic Normality of the GMM and M Estimators," Papers 2006.02541, arXiv.org, revised Sep 2020.
    7. Marcel Carcea & Robert Serfling, 2015. "A Gini Autocovariance Function for Time Series Modelling," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 817-838, November.
    8. Peter C.B. Phillips & Mico Loretan, 1990. "Testing Covariance Stationarity Under Moment Condition Failure with an Application to Common Stock Returns," Cowles Foundation Discussion Papers 947, Cowles Foundation for Research in Economics, Yale University.
    9. Kim, Mihyun & Kokoszka, Piotr, 2022. "Extremal dependence measure for functional data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    10. Sai-Hua Huang & Tian-Xiao Pang & Chengguo Weng, 2014. "Limit Theory for Moderate Deviations from a Unit Root Under Innovations with a Possibly Infinite Variance," Methodology and Computing in Applied Probability, Springer, vol. 16(1), pages 187-206, March.
    11. Amit Shelef & Edna Schechtman, 2019. "A Gini-based time series analysis and test for reversibility," Statistical Papers, Springer, vol. 60(3), pages 687-716, June.
    12. Kokoszka, Piotr S. & Taqqu, Murad S., 1995. "Fractional ARIMA with stable innovations," Stochastic Processes and their Applications, Elsevier, vol. 60(1), pages 19-47, November.
    13. Akashi, Fumiya & Taniguchi, Masanobu & Monti, Anna Clara, 2020. "Robust causality test of infinite variance processes," Journal of Econometrics, Elsevier, vol. 216(1), pages 235-245.
    14. Basrak, Bojan & Davis, Richard A. & Mikosch, Thomas, 1999. "The sample ACF of a simple bilinear process," Stochastic Processes and their Applications, Elsevier, vol. 83(1), pages 1-14, September.
    15. Barczy, Mátyás & Basrak, Bojan & Kevei, Péter & Pap, Gyula & Planinić, Hrvoje, 2021. "Statistical inference of subcritical strongly stationary Galton–Watson processes with regularly varying immigration," Stochastic Processes and their Applications, Elsevier, vol. 132(C), pages 33-75.

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