IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v139y2019icp164-177.html
   My bibliography  Save this article

Regularized joint estimation of related vector autoregressive models

Author

Listed:
  • Skripnikov, A.
  • Michailidis, G.

Abstract

In a number of applications, one has access to high-dimensional time series data on several related subjects. A motivating application area comes from the neuroimaging field, such as brain fMRI time series data, obtained from various groups of subjects (cases/controls) with a specific neurological disorder. The problem of regularized joint estimation of multiple related Vector Autoregressive (VAR) models is discussed, leveraging a group lasso penalty in addition to a regular lasso one, so as to increase statistical efficiency of the estimates by borrowing strength across the models. A modeling framework is developed that it allows for both group-level and subject-specific effects for related subjects, using a group lasso penalty to estimate the former. An estimation procedure is introduced, whose performance is illustrated on synthetic data and compared to other state-of-the-art methods. Moreover, the proposed approach is employed for the analysis of resting state fMRI data. In particular, a group-level descriptive analysis is conducted for brain inter-regional temporal effects of Attention Deficit Hyperactive Disorder (ADHD) patients as opposed to controls, with the data available from the ADHD-200 Global Competition repository.

Suggested Citation

  • Skripnikov, A. & Michailidis, G., 2019. "Regularized joint estimation of related vector autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 164-177.
  • Handle: RePEc:eee:csdana:v:139:y:2019:i:c:p:164-177
    DOI: 10.1016/j.csda.2019.05.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947319301185
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2019.05.007?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. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    2. Wolfgang Härdle & Joel Horowitz & Jens‐Peter Kreiss, 2003. "Bootstrap Methods for Time Series," International Statistical Review, International Statistical Institute, vol. 71(2), pages 435-459, August.
    3. Nicolai Meinshausen & Peter Bühlmann, 2010. "Stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 417-473, September.
    4. Jian Guo & Elizaveta Levina & George Michailidis & Ji Zhu, 2011. "Joint estimation of multiple graphical models," Biometrika, Biometrika Trust, vol. 98(1), pages 1-15.
    5. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    6. Jochen Gorski & Frank Pfeuffer & Kathrin Klamroth, 2007. "Biconvex sets and optimization with biconvex functions: a survey and extensions," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 66(3), pages 373-407, December.
    7. Patrick Danaher & Pei Wang & Daniela M. Witten, 2014. "The joint graphical lasso for inverse covariance estimation across multiple classes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(2), pages 373-397, March.
    8. Richard E. Wendell & Arthur P. Hurter, 1976. "Minimization of a Non-Separable Objective Function Subject to Disjoint Constraints," Operations Research, INFORMS, vol. 24(4), pages 643-657, August.
    9. Jian Huang & Shuange Ma & Huiliang Xie & Cun-Hui Zhang, 2009. "A group bridge approach for variable selection," Biometrika, Biometrika Trust, vol. 96(2), pages 339-355.
    10. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    11. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    12. Sutao Song & Zhichao Zhan & Zhiying Long & Jiacai Zhang & Li Yao, 2011. "Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-11, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lai, Wei-Ting & Chen, Ray-Bing & Chen, Ying & Koch, Thorsten, 2022. "Variational Bayesian inference for network autoregression models," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).

    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. Skripnikov, A. & Michailidis, G., 2019. "Joint estimation of multiple network Granger causal models," Econometrics and Statistics, Elsevier, vol. 10(C), pages 120-133.
    2. Jianyu Liu & Wei Sun & Yufeng Liu, 2019. "Joint skeleton estimation of multiple directed acyclic graphs for heterogeneous population," Biometrics, The International Biometric Society, vol. 75(1), pages 36-47, March.
    3. Dong Liu & Changwei Zhao & Yong He & Lei Liu & Ying Guo & Xinsheng Zhang, 2023. "Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix‐variate fMRI data," Biometrics, The International Biometric Society, vol. 79(3), pages 2246-2259, September.
    4. Guanhao Feng & Nicholas Polson, 2020. "Regularizing Bayesian predictive regressions," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 591-608, December.
    5. Lai, Wei-Ting & Chen, Ray-Bing & Chen, Ying & Koch, Thorsten, 2022. "Variational Bayesian inference for network autoregression models," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    6. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.
    7. Dallakyan, Aramayis & Kim, Rakheon & Pourahmadi, Mohsen, 2022. "Time series graphical lasso and sparse VAR estimation," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    8. Calvo-Pardo, Hector & Mancini, Tullio & Olmo, Jose, 2021. "Granger causality detection in high-dimensional systems using feedforward neural networks," International Journal of Forecasting, Elsevier, vol. 37(2), pages 920-940.
    9. Diego Vidaurre & Concha Bielza & Pedro Larrañaga, 2013. "A Survey of L1 Regression," International Statistical Review, International Statistical Institute, vol. 81(3), pages 361-387, December.
    10. Aaron Hudson & Ali Shojaie, 2022. "Covariate-Adjusted Inference for Differential Analysis of High-Dimensional Networks," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 345-388, June.
    11. Gaynanova, Irina & Wang, Tianying, 2019. "Sparse quadratic classification rules via linear dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 278-299.
    12. Caruso, Alberto & Reichlin, Lucrezia & Ricco, Giovanni, 2019. "Financial and fiscal interaction in the Euro Area crisis: This time was different," European Economic Review, Elsevier, vol. 119(C), pages 333-355.
    13. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    14. Pooyan Amir-Ahmadi & Christian Matthes & Mu-Chun Wang, 2020. "Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 124-136, January.
    15. Salzmann, Leonard, 2020. "The Impact of Uncertainty and Financial Shocks in Recessions and Booms," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224588, Verein für Socialpolitik / German Economic Association.
    16. Gary Koop & Dimitris Korobilis, 2019. "Forecasting with High‐Dimensional Panel VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 937-959, October.
    17. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    18. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    19. Miescu, Mirela & Rossi, Raffaele, 2021. "COVID-19-induced shocks and uncertainty," European Economic Review, Elsevier, vol. 139(C).
    20. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.

    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:eee:csdana:v:139:y:2019:i:c:p:164-177. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.