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Long memory via networking

Author

Listed:
  • Susanne M. Schennach

    (Institute for Fiscal Studies and Brown University)

Abstract

Many time-series data are known to exhibit 'long memory', that is, they have an autocorrelation function that decays very slowly with lag. This behaviour has traditionally been attributed to either aggregation of heterogenous processes, nonlinearity, learning dynamics, regime switching, structural breaks, unit roots or fractional Brownian motion. This paper identifies an entirely different mechanism for long memory generation by showing that it can naturally arise when a large number of simply linear homogenous economic subsystems with a short memory are interconnected to form a network such that the outputs of each of the subsystem are fed into the inputs of others. This networking picture yields a type of aggregation that is not merely additive, resulting in a collective behaviour that is richer than that of individual subsystems. Interestingly, the long memory behaviour is found to be almost entirely determined by the geometry of the network while being relatively insensitive to the specific behaviour of individual agents.

Suggested Citation

  • Susanne M. Schennach, 2013. "Long memory via networking," CeMMAP working papers CWP13/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:13/13
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    File URL: http://www.cemmap.ac.uk/wps/cwp131313.pdf
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    Cited by:

    1. Daron Acemoglu & Ufuk Akcigit & William Kerr, 2016. "Networks and the Macroeconomy: An Empirical Exploration," NBER Macroeconomics Annual, University of Chicago Press, vol. 30(1), pages 273-335.
    2. Bauwens, Luc & Chevillon, Guillaume & Laurent, Sébastien, 2023. "We modeled long memory with just one lag!," Journal of Econometrics, Elsevier, vol. 236(1).
    3. Anna Mikusheva & Mikkel Sølvsten, 2025. "Linear regression with weak exogeneity," Quantitative Economics, Econometric Society, vol. 16(2), pages 367-403, May.
    4. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    5. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    6. Chevillon, Guillaume & Hecq, Alain & Laurent, Sébastien, 2018. "Generating univariate fractional integration within a large VAR(1)," Journal of Econometrics, Elsevier, vol. 204(1), pages 54-65.
    7. Christis Katsouris, 2023. "Limit Theory under Network Dependence and Nonstationarity," Papers 2308.01418, arXiv.org, revised Aug 2023.
    8. Chevillon, Guillaume & Mavroeidis, Sophocles, 2017. "Learning can generate long memory," Journal of Econometrics, Elsevier, vol. 198(1), pages 1-9.
    9. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Economics and Statistics Working Papers 8-2022, Singapore Management University, School of Economics.
    10. Christis Katsouris, 2024. "Robust Estimation in Network Vector Autoregression with Nonstationary Regressors," Papers 2401.04050, arXiv.org.
    11. Shikta Singh & Supun Chandrasena & Yue Shi & Abdullah Alhussaini & Claude Diebolt & Martin Enilov & Tapas Mishra, 2026. "A Learning Model with Memory in the Financial Markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 31(1), pages 1203-1213, January.
    12. Chevillon, Guillaume & Hecq , Alain & Laurent, Sébastien, 2015. "Long Memory Through Marginalization of Large Systems and Hidden Cross-Section Dependence," ESSEC Working Papers WP1507, ESSEC Research Center, ESSEC Business School.
    13. Castro, Tomas del Barrio & Escribano, Alvaro & Sibbertsen, Philipp, 2025. "Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data," Energy Economics, Elsevier, vol. 147(C).
    14. Federico Carlini & Paolo Santucci de Magistris, 2019. "Resuscitating the co-fractional model of Granger (1986)," CREATES Research Papers 2019-02, Department of Economics and Business Economics, Aarhus University.
    15. Barrdear, John, 2014. "Peering into the mist: social learning over an opaque observation network," LSE Research Online Documents on Economics 58083, London School of Economics and Political Science, LSE Library.
    16. Hongwei Chuang, 2015. "Correlation Persistence in Financial Markets: A Network Theory Approach," DSSR Discussion Papers 33, Graduate School of Economics and Management, Tohoku University.
    17. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Nieves Carmona-González, 2025. "Atmospheric Pollution in 10 US Cities: Trends and Persistence," CESifo Working Paper Series 11957, CESifo.
    18. Proietti, Tommaso & Maddanu, Federico, 2024. "Modelling cycles in climate series: The fractional sinusoidal waveform process," Journal of Econometrics, Elsevier, vol. 239(1).
    19. Susanne M. Schennach, 2018. "Long Memory via Networking," Econometrica, Econometric Society, vol. 86(6), pages 2221-2248, November.
    20. Zanetti Chini, Emilio, 2025. "Judgment can spur long memory," Journal of Economic Dynamics and Control, Elsevier, vol. 170(C).

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