IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v65y2025i1d10.1007_s10614-024-10568-7.html
   My bibliography  Save this article

A Smooth Transition Autoregressive Model for Matrix-Variate Time Series

Author

Listed:
  • Andrea Bucci

    (University of Macerata)

Abstract

In this paper, we present a new approach for modelling matrix-variate time series data that accounts for smooth changes in the dynamics of matrices. Although stylized facts in several fields suggest the existence of smooth nonlinearities, the existing matrix-variate models do not account for regime switches that are not abrupt. To address this gap, we introduce the matrix smooth transition autoregressive model, a flexible regime-switching model capable of capturing abrupt, smooth and no regime changes in matrix-valued data. We provide a thorough examination of the estimation process and evaluate the finite-sample performance of the matrix-variate smooth transition autoregressive model estimators with simulated data. Finally, the model is applied to real-world data.

Suggested Citation

  • Andrea Bucci, 2025. "A Smooth Transition Autoregressive Model for Matrix-Variate Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 429-458, January.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:1:d:10.1007_s10614-024-10568-7
    DOI: 10.1007/s10614-024-10568-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10568-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-024-10568-7?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hillebrand Eric & Medeiros Marcelo C. & Xu Junyue, 2013. "Asymptotic Theory for Regressions with Smoothly Changing Parameters," Journal of Time Series Econometrics, De Gruyter, vol. 5(2), pages 133-162, April.
    2. Liu, Xialu & Chen, Rong, 2020. "Threshold factor models for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 216(1), pages 53-70.
    3. Giovanni Angelini & Emanuele Bacchiocchi & Giovanni Caggiano & Luca Fanelli, 2019. "Uncertainty across volatility regimes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 437-455, April.
    4. Janus, Jakub, 2021. "The COVID-19 shock and long-term interest rates in emerging market economies," Finance Research Letters, Elsevier, vol. 43(C).
    5. Lundbergh, Stefan & Terasvirta, Timo & van Dijk, Dick, 2003. "Time-Varying Smooth Transition Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 104-121, January.
    6. Frauke Schleer, 2015. "Finding Starting-Values for the Estimation of Vector STAR Models," Econometrics, MDPI, vol. 3(1), pages 1-26, January.
    7. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    8. Kirstin Hubrich & Timo Teräsvirta, 2013. "Thresholds and Smooth Transitions in Vector Autoregressive Models," CREATES Research Papers 2013-18, Department of Economics and Business Economics, Aarhus University.
    9. Chen, Rong & Xiao, Han & Yang, Dan, 2021. "Autoregressive models for matrix-valued time series," Journal of Econometrics, Elsevier, vol. 222(1), pages 539-560.
    10. Terasvirta, Timo & Yang, Yukai, 2014. "Specification, estimation and evaluation of vector smooth transition autoregressive models with applications," LIDAM Discussion Papers CORE 2014062, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. Xialu Liu & Elynn Y. Chen, 2022. "Identification and estimation of threshold matrix‐variate factor models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1383-1417, September.
    12. Jian Liu, 1992. "Spectral Radius, Kronecker Products And Stationarity," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(4), pages 319-325, July.
    13. Ahmed, Maruf Yakubu & Sarkodie, Samuel Asumadu, 2021. "COVID-19 pandemic and economic policy uncertainty regimes affect commodity market volatility," Resources Policy, Elsevier, vol. 74(C).
    14. Hua Zhou & Lexin Li & Hongtu Zhu, 2013. "Tensor Regression with Applications in Neuroimaging Data Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 540-552, June.
    15. Wang, Dong & Liu, Xialu & Chen, Rong, 2019. "Factor models for matrix-valued high-dimensional time series," Journal of Econometrics, Elsevier, vol. 208(1), pages 231-248.
    16. Anderson, Heather M. & Vahid, Farshid, 1998. "Testing multiple equation systems for common nonlinear components," Journal of Econometrics, Elsevier, vol. 84(1), pages 1-36, May.
    17. Chenlei Leng & Cheng Yong Tang, 2012. "Sparse Matrix Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1187-1200, September.
    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. Andrea Bucci, 2022. "A smooth transition autoregressive model for matrix-variate time series," Papers 2212.08615, arXiv.org.
    2. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    3. Ben Cheikh, Nidhaleddine & Ben Naceur, Sami & Kanaan, Oussama & Rault, Christophe, 2021. "Investigating the asymmetric impact of oil prices on GCC stock markets," Economic Modelling, Elsevier, vol. 102(C).
    4. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Fed’s unconventional monetary policy and risk spillover in the US financial markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 78(C), pages 42-52.
    5. Kotz Hans-Helmut & Semmler Willi & Tahri Ibrahim, 2018. "Financial fragmentation and the monetary transmission mechanism in the euro area: a smooth transition VAR approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-19, December.
    6. Schleer, Frauke & Semmler, Willi, 2015. "Financial sector and output dynamics in the euro area: Non-linearities reconsidered," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 235-263.
    7. Balcilar, Mehmet & Usman, Ojonugwa & Duman, Gazi Murat, 2024. "Nonlinear network connectedness: Assessing financial risk transmission in MENA and influence of external financial conditions," Emerging Markets Review, Elsevier, vol. 62(C).
    8. Timo Teräsvirta, 2017. "Nonlinear models in macroeconometrics," CREATES Research Papers 2017-32, Department of Economics and Business Economics, Aarhus University.
    9. Kirstin Hubrich & Timo Teräsvirta, 2013. "Thresholds and Smooth Transitions in Vector Autoregressive Models," CREATES Research Papers 2013-18, Department of Economics and Business Economics, Aarhus University.
    10. Glen Livingston Jr & Darfiana Nur, 2020. "Bayesian estimation and model selection of a multivariate smooth transition autoregressive model," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.
    11. Andrea Bucci & Giulio Palomba & Eduardo Rossi, 2019. "Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach," Working Papers 440, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    12. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben, 2023. "Investigating the dynamics of crude oil and clean energy markets in times of geopolitical tensions," Energy Economics, Elsevier, vol. 124(C).
    13. Raza, Syed Ali & Masood, Amna & Benkraiem, Ramzi & Urom, Christian, 2023. "Forecasting the volatility of precious metals prices with global economic policy uncertainty in pre and during the COVID-19 period: Novel evidence from the GARCH-MIDAS approach," Energy Economics, Elsevier, vol. 120(C).
    14. Gabriel Arce‐Alfaro & Boris Blagov, 2023. "Monetary Policy Uncertainty and Inflation Expectations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(1), pages 70-94, February.
    15. Yujia, Li & Zixiang, Zhu & Ming, Che, 2024. "Exploring the relationship between China's economic policy uncertainty and business cycles: Exogenous impulse or endogenous responses?," Emerging Markets Review, Elsevier, vol. 58(C).
    16. Emilio Zanetti Chini, 2013. "Generalizing smooth transition autoregressions," CREATES Research Papers 2013-32, Department of Economics and Business Economics, Aarhus University.
    17. Ruofan Yu & Rong Chen & Han Xiao & Yuefeng Han, 2024. "Dynamic Matrix Factor Models for High Dimensional Time Series," Papers 2407.05624, arXiv.org.
    18. González, Andrés & Teräsvirta, Timo & van Dijk, Dick & Yang, Yukai, 2005. "Panel Smooth Transition Regression Models," SSE/EFI Working Paper Series in Economics and Finance 604, Stockholm School of Economics, revised 11 Oct 2017.
    19. Caggiano, Giovanni & Castelnuovo, Efrem & Kima, Richard, 2020. "The global effects of Covid-19-induced uncertainty," Economics Letters, Elsevier, vol. 194(C).
    20. Rivolta, Giulia & Trecroci, Carmine, 2020. "Measuring the effects of U.S. uncertainty and monetary conditions on EMEs' macroeconomic dynamics," MPRA Paper 99403, University Library of Munich, Germany.

    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:kap:compec:v:65:y:2025:i:1:d:10.1007_s10614-024-10568-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.