IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v28y2013i2p493-504.html
   My bibliography  Save this article

Lag weighted lasso for time series model

Author

Listed:
  • Heewon Park
  • Fumitake Sakaori

Abstract

The adaptive lasso can consistently identify the true model in regression model. However, the adaptive lasso cannot account for lag effects, which are essential for a time series model. Consequently, the adaptive lasso can not reflect certain properties of a time series model. To improve the forecast accuracy of a time series model, we propose a lag weighted lasso. The lag weighted lasso imposes different penalties on each coefficient based on weights that reflect not only the coefficients size but also the lag effects. Simulation studies and a real example show that the proposed method is superior to both the lasso and the adaptive lasso in forecast accuracy. Copyright Springer-Verlag 2013

Suggested Citation

  • Heewon Park & Fumitake Sakaori, 2013. "Lag weighted lasso for time series model," Computational Statistics, Springer, vol. 28(2), pages 493-504, April.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:2:p:493-504
    DOI: 10.1007/s00180-012-0313-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-012-0313-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00180-012-0313-5?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. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Romy R. Ravines & Alexandra M. Schmidt & Helio S. Migon, 2006. "Revisiting distributed lag models through a Bayesian perspective," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 22(2), pages 193-210, March.
    3. Wang, Hansheng & Leng, Chenlei, 2008. "A note on adaptive group lasso," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5277-5286, August.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. Ming Yuan & Yi Lin, 2007. "On the non‐negative garrotte estimator," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 143-161, April.
    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. Heewon Park & Sadanori Konishi, 2017. "Principal component selection via adaptive regularization method and generalized information criterion," Statistical Papers, Springer, vol. 58(1), pages 147-160, March.
    2. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    3. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(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. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
    2. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    3. Fei Jin & Lung-fei Lee, 2018. "Lasso Maximum Likelihood Estimation of Parametric Models with Singular Information Matrices," Econometrics, MDPI, vol. 6(1), pages 1-24, February.
    4. Wang Chamont & Gevertz Jana L., 2016. "Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 321-347, August.
    5. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    6. Xue Wu & Chixiang Chen & Zheng Li & Lijun Zhang & Vernon M. Chinchilli & Ming Wang, 2024. "A three-stage approach to identify biomarker signatures for cancer genetic data with survival endpoints," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 863-883, July.
    7. Justin B. Post & Howard D. Bondell, 2013. "Factor Selection and Structural Identification in the Interaction ANOVA Model," Biometrics, The International Biometric Society, vol. 69(1), pages 70-79, March.
    8. Jin, Fei & Lee, Lung-fei, 2018. "Irregular N2SLS and LASSO estimation of the matrix exponential spatial specification model," Journal of Econometrics, Elsevier, vol. 206(2), pages 336-358.
    9. Jonathan Boss & Alexander Rix & Yin‐Hsiu Chen & Naveen N. Narisetty & Zhenke Wu & Kelly K. Ferguson & Thomas F. McElrath & John D. Meeker & Bhramar Mukherjee, 2021. "A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    10. Wang, Kangning & Li, Shaomin, 2021. "Robust distributed modal regression for massive data," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    11. Hu, Jianhua & Liu, Xiaoqian & Liu, Xu & Xia, Ningning, 2022. "Some aspects of response variable selection and estimation in multivariate linear regression," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    12. Caiya Zhang & Yanbiao Xiang, 2016. "On the oracle property of adaptive group Lasso in high-dimensional linear models," Statistical Papers, Springer, vol. 57(1), pages 249-265, March.
    13. Bang, Sungwan & Jhun, Myoungshic, 2012. "Simultaneous estimation and factor selection in quantile regression via adaptive sup-norm regularization," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 813-826.
    14. Guo, Xiao & Zhang, Hai & Wang, Yao & Wu, Jiang-Lun, 2015. "Model selection and estimation in high dimensional regression models with group SCAD," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 86-92.
    15. Zhaoping Hong & Yuao Hu & Heng Lian, 2013. "Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(7), pages 887-908, October.
    16. Fang, Xiaolei & Paynabar, Kamran & Gebraeel, Nagi, 2017. "Multistream sensor fusion-based prognostics model for systems with single failure modes," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 322-331.
    17. Qian, Junhui & Su, Liangjun, 2016. "Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso," Journal of Econometrics, Elsevier, vol. 191(1), pages 86-109.
    18. Lu, Xun & Su, Liangjun, 2016. "Shrinkage estimation of dynamic panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 190(1), pages 148-175.
    19. Xianyi Wu & Xian Zhou, 2019. "On Hodges’ superefficiency and merits of oracle property in model selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1093-1119, October.
    20. Belli, Edoardo, 2022. "Smoothly adaptively centered ridge estimator," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

    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:spr:compst:v:28:y:2013:i:2:p:493-504. 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.