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On aggregation of strongly dependent time series

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  • Jan Beran
  • Haiyan Liu
  • Sucharita Ghosh

Abstract

We consider cross‐sectional aggregation of time series with long‐range dependence. This question arises for instance from the statistical analysis of networks where aggregation is defined via routing matrices. Asymptotically, aggregation turns out to increase dependence substantially, transforming a hyperbolic decay of autocorrelations to a slowly varying rate. This effect has direct consequences for statistical inference. For instance, unusually slow rates of convergence for nonparametric trend estimators and nonstandard formulas for optimal bandwidths are obtained. The situation changes, when time‐dependent aggregation is applied. Suitably chosen time‐dependent aggregation schemes can preserve a hyperbolic rate or even eliminate autocorrelations completely.

Suggested Citation

  • Jan Beran & Haiyan Liu & Sucharita Ghosh, 2020. "On aggregation of strongly dependent time series," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 690-710, September.
    • Handle: RePEc:bla:scjsta:v:47:y:2020:i:3:p:690-710
    DOI: 10.1111/sjos.12421
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    References listed on IDEAS

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    1. Davidson, James & Sibbertsen, Philipp, 2005. "Generating schemes for long memory processes: regimes, aggregation and linearity," Journal of Econometrics, Elsevier, vol. 128(2), pages 253-282, October.
    2. Kazakevicius, Vytautas & Leipus, Remigijus & Viano, Marie-Claude, 2004. "Stability of random coefficient ARCH models and aggregation schemes," Journal of Econometrics, Elsevier, vol. 120(1), pages 139-158, May.
    3. Paolo Zaffaroni, 2007. "Contemporaneous aggregation of GARCH processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(4), pages 521-544, July.
    4. Terence Tai-Leung Chong, 2006. "The polynomial aggregated AR(1) model," Econometrics Journal, Royal Economic Society, vol. 9(1), pages 98-122, March.
    5. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    6. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    7. Karim Abadir & Gabriel Talmain, 2002. "Aggregation, Persistence and Volatility in a Macro Model," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(4), pages 749-779.
    8. Zaffaroni, Paolo, 2004. "Contemporaneous aggregation of linear dynamic models in large economies," Journal of Econometrics, Elsevier, vol. 120(1), pages 75-102, May.
    9. Marco Lippi & Paolo Zaffaroni, 1998. "Aggregation of Simple Linear Dynamics: Exact Asymptotic Results," STICERD - Econometrics Paper Series 350, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    10. Pilipauskaitė, Vytautė & Surgailis, Donatas, 2015. "Joint aggregation of random-coefficient AR(1) processes with common innovations," Statistics & Probability Letters, Elsevier, vol. 101(C), pages 73-82.
    11. Edoardo M. Airoldi & Alexander W. Blocker, 2013. "Estimating Latent Processes on a Network From Indirect Measurements," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 149-164, March.
    12. Giraitis, Liudas & Leipus, Remigijus & Surgailis, Donatas, 2010. "Aggregation Of The Random Coefficient Glarch(1,1) Process," Econometric Theory, Cambridge University Press, vol. 26(2), pages 406-425, April.
    13. Zaffaroni, Paolo, 2007. "Aggregation and memory of models of changing volatility," Journal of Econometrics, Elsevier, vol. 136(1), pages 237-249, January.
    14. Georges Oppenheim & Marie‐Claude Viano, 2004. "Aggregation of random parameters Ornstein‐Uhlenbeck or AR processes: some convergence results," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(3), pages 335-350, May.
    15. Beran, Jan & Schützner, Martin & Ghosh, Sucharita, 2010. "From short to long memory: Aggregation and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2432-2442, November.
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    1. Hidekazu Yoshioka & Yumi Yoshioka, 2024. "Statistical evaluation of a long‐memory process using the generalized entropic value‐at‐risk," Environmetrics, John Wiley & Sons, Ltd., vol. 35(4), June.

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