IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v36y2025i2ne2896.html

The Effect of the North Atlantic Oscillation on Monthly Precipitation in Selected European Locations: A Non‐Linear Time Series Approach

Author

Listed:
  • Changli He
  • Jian Kang
  • Annastiina Silvennoinen
  • Timo Teräsvirta

Abstract

In this article, the relationship between the monthly precipitation in 30 European cities and towns, and two Algerian ones, and the North Atlantic Oscillation (NAO) index is characterized using the Vector Seasonal Shifting Mean and Covariance Autoregressive model, extended to contain exogenous variables. The results, based on monthly time series from 1851 up until 2020, include shifting monthly means for the rainfall series and the estimated coefficients of the exogenous NAO variable. They suggest that in the north and the west, the amount of rain in the boreal winter months has increased or stayed the same during the observation period, whereas in the Mediterranean area, there have been systematic decreases. Results on the North Atlantic Oscillation indicate that the NAO has its strongest effect on precipitation during the winter months. The (negative) effect is particularly strong in Western Europe, Lisbon, and the Mediterranean rim. In contrast, the effect in northern locations is positive for the winter months. The constancy of error variances and correlations is tested and, if rejected, the time‐varying alternative is estimated. A spatial relationship between the error correlations and the distance between the corresponding pairs of cities is estimated.

Suggested Citation

  • Changli He & Jian Kang & Annastiina Silvennoinen & Timo Teräsvirta, 2025. "The Effect of the North Atlantic Oscillation on Monthly Precipitation in Selected European Locations: A Non‐Linear Time Series Approach," Environmetrics, John Wiley & Sons, Ltd., vol. 36(2), March.
  • Handle: RePEc:wly:envmet:v:36:y:2025:i:2:n:e2896
    DOI: 10.1002/env.2896
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.2896
    Download Restriction: no

    File URL: https://libkey.io/10.1002/env.2896?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. He, Changli & Kang, Jian & Silvennoinen, Annastiina & Teräsvirta, Timo, 2023. "Long monthly European temperature series and the North Atlantic Oscillation," Energy Economics, Elsevier, vol. 126(C).
    2. He, Changli & Kang, Jian & Teräsvirta, Timo & Zhang, Shuhua, 2019. "The shifting seasonal mean autoregressive model and seasonality in the Central England monthly temperature series, 1772–2016," Econometrics and Statistics, Elsevier, vol. 12(C), pages 1-24.
    3. He, Changli & Kang, Jian & Silvennoinen, Annastiina & Teräsvirta, Timo, 2024. "Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model," Journal of Econometrics, Elsevier, vol. 239(1).
    4. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. He, Changli & Kang, Jian & Silvennoinen, Annastiina & Teräsvirta, Timo, 2023. "Long monthly European temperature series and the North Atlantic Oscillation," Energy Economics, Elsevier, vol. 126(C).
    2. He, Changli & Kang, Jian & Teräsvirta, Timo & Zhang, Shuhua, 2021. "Comparing long monthly Chinese and selected European temperature series using the Vector Seasonal Shifting Mean and Covariance Autoregressive model," Energy Economics, Elsevier, vol. 97(C).
    3. Bodart, Vincent & Reding, Paul, 1999. "Exchange rate regime, volatility and international correlations on bond and stock markets," Journal of International Money and Finance, Elsevier, vol. 18(1), pages 133-151, January.
    4. Markus Haas, 2018. "A note on the absolute moments of the bivariate normal distribution," Economics Bulletin, AccessEcon, vol. 38(1), pages 650-656.
    5. Pedersen, Rasmus Søndergaard, 2016. "Targeting Estimation Of Ccc-Garch Models With Infinite Fourth Moments," Econometric Theory, Cambridge University Press, vol. 32(2), pages 498-531, April.
    6. Cavit Pakel & Neil Shephard & Kevin Sheppard & Robert F. Engle, 2021. "Fitting Vast Dimensional Time-Varying Covariance Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 652-668, July.
    7. Weidong Lin & Jose Olmo & Abderrahim Taamouti, 2025. "Portfolio Selection under Systemic Risk," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 57(4), pages 905-949, June.
    8. Mohamed Es-Sanoun & Jude Gohou & Mounir Benboubker, 2023. "Testing of Herd Behavior In african Stock Markets During COVID-19 Pandemic [Essai de vérification du comportement mimétique dans les marchés boursiers africains au cours de la crise de covid-19]," Post-Print hal-04144289, HAL.
    9. Chia-Lin Chang & Yiying Li & Michael McAleer, 2018. "Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice," Energies, MDPI, vol. 11(6), pages 1-19, June.
    10. Liow, Kim Hiang & Huang, Yuting, 2018. "The dynamics of volatility connectedness in international real estate investment trusts," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 195-210.
    11. repec:ipg:wpaper:2014-442 is not listed on IDEAS
    12. Charlot, Philippe & Darné, Olivier & Moussa, Zakaria, 2016. "Commodity returns co-movements: Fundamentals or “style” effect?," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 130-160.
    13. Bouri, Elie & Gabauer, David & Gupta, Rangan & Tiwari, Aviral Kumar, 2021. "Volatility connectedness of major cryptocurrencies: The role of investor happiness," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    14. Arturo Lorenzo Valdés & Rocío Durán Vázquez & Leticia Armenta Fraire, 2012. "Conditional Correlation Between Oil and Stock Market Returns: The Case of Mexico," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 7(1), pages 49-63, Enero-Jun.
    15. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    16. Małgorzata Doman, 2005. "The Co-movement Between Returns of Foreign Exchange Rates in the Central European Countries," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Władysław Milo & Piotr Wdowiński (ed.), Acta Universitatis Lodziensis. Folia Oeconomica nr 192/2005 - Issues in Modeling, Forecasting and Decision-Making in Financial Markets, edition 1, volume 127, chapter 10, pages 157-175, University of Lodz.
    17. Pourahmadi, Mohsen & Daniels, Michael J. & Park, Trevor, 2007. "Simultaneous modelling of the Cholesky decomposition of several covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 568-587, March.
    18. Morana, Claudio & Sbrana, Giacomo, "undated". "Temperature Anomalies, Radiative Forcing and ENSO," MITP: Mitigation, Innovation and Transformation Pathways 253732, Fondazione Eni Enrico Mattei (FEEM).
    19. Manabu Asai & Michael McAleer, 2009. "Dynamic Conditional Correlations for Asymmetric Processes," CARF F-Series CARF-F-168, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    20. Yu‐Sheng Lai, 2022. "High‐frequency data and stock–bond investing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1623-1638, December.
    21. Ülkü, Numan & Weber, Enzo, 2013. "Identifying the interaction between stock market returns and trading flows of investor types: Looking into the day using daily data," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2733-2749.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:envmet:v:36:y:2025:i:2:n:e2896. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1180-4009/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.