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On the Validity of Granger Causality for Ecological Count Time Series

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  • Konstantinos G. Papaspyropoulos

    (Laboratory of Forest Economics, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Dimitris Kugiumtzis

    (Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

Abstract

Knowledge of causal relationships is fundamental for understanding the dynamic mechanisms of ecological systems. To detect such relationships from multivariate time series, Granger causality, an idea first developed in econometrics, has been formulated in terms of vector autoregressive (VAR) models. Granger causality for count time series, often seen in ecology, has rarely been explored, and this may be due to the difficulty in estimating autoregressive models on multivariate count time series. The present research investigates the appropriateness of VAR-based Granger causality for ecological count time series by conducting a simulation study using several systems of different numbers of variables and time series lengths. VAR-based Granger causality for count time series (DVAR) seems to be estimated efficiently even for two counts in long time series. For all the studied time series lengths, DVAR for more than eight counts matches the Granger causality effects obtained by VAR on the continuous-valued time series well. The positive results, also in two ecological time series, suggest the use of VAR-based Granger causality for assessing causal relationships in real-world count time series even with few distinct integer values or many zeros.

Suggested Citation

  • Konstantinos G. Papaspyropoulos & Dimitris Kugiumtzis, 2024. "On the Validity of Granger Causality for Ecological Count Time Series," Econometrics, MDPI, vol. 12(2), pages 1-21, May.
    • Handle: RePEc:gam:jecnmx:v:12:y:2024:i:2:p:13-:d:1391256
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    References listed on IDEAS

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    1. Jiajie Kong & Robert Lund, 2023. "Seasonal count time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 93-124, January.
    2. Ali Ahmad & Christian Francq, 2016. "Poisson QMLE of Count Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 291-314, May.
    3. Catania, Leopoldo & Di Mari, Roberto, 2021. "Hierarchical Markov-switching models for multivariate integer-valued time-series," Journal of Econometrics, Elsevier, vol. 221(1), pages 118-137.
    4. Marcelo Bourguignon & Josemar Rodrigues & Manoel Santos-Neto, 2019. "Extended Poisson INAR(1) processes with equidispersion, underdispersion and overdispersion," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(1), pages 101-118, January.
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