IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v134y2015icp107-110.html
   My bibliography  Save this article

Testing change in volatility using panel data

Author

Listed:
  • Shi, Yutang

Abstract

The focus of this paper is to test the possible changes in the volatility of panel data. The test statistic is derived from a likelihood argument and it is based on the CUSUM method. Asymptotic distribution is derived under the no change null hypothesis and the consistency of the test is also established. Monte Carlo simulation shows the effectiveness and improvement of the proposed procedure over some of the existing testing procedures.

Suggested Citation

  • Shi, Yutang, 2015. "Testing change in volatility using panel data," Economics Letters, Elsevier, vol. 134(C), pages 107-110.
    • Handle: RePEc:eee:ecolet:v:134:y:2015:i:c:p:107-110
    DOI: 10.1016/j.econlet.2015.06.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176515002669
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2015.06.016?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. Li, Fuxiao & Tian, Zheng & Xiao, Yanting & Chen, Zhanshou, 2015. "Variance change-point detection in panel data models," Economics Letters, Elsevier, vol. 126(C), pages 140-143.
    2. Bai, Jushan, 2010. "Common breaks in means and variances for panel data," Journal of Econometrics, Elsevier, vol. 157(1), pages 78-92, July.
    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. Eunju Hwang & Dong Wan Shin, 2017. "Stationary bootstrapping for common mean change detection in cross-sectionally dependent panels," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(6), pages 767-787, November.

    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. Eunju Hwang & Dong Wan Shin, 2017. "Stationary bootstrapping for common mean change detection in cross-sectionally dependent panels," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(6), pages 767-787, November.
    2. Guido Bulligan & Francesco Corsello & Stefano Neri & Alex Tagliabracci, 2021. "De-anchored long-term inflation expectations in a low growth, low rate environment," Questioni di Economia e Finanza (Occasional Papers) 624, Bank of Italy, Economic Research and International Relations Area.
    3. Smith, Simon C. & Timmermann, Allan & Zhu, Yinchu, 2019. "Variable selection in panel models with breaks," Journal of Econometrics, Elsevier, vol. 212(1), pages 323-344.
    4. Pierre L. Siklos, 2020. "Looking into the Rear-View Mirror: Lessons from Japan for the Eurozone and the U.S?," IMES Discussion Paper Series 20-E-02, Institute for Monetary and Economic Studies, Bank of Japan.
    5. Barigozzi, Matteo & Trapani, Lorenzo, 2020. "Sequential testing for structural stability in approximate factor models," Stochastic Processes and their Applications, Elsevier, vol. 130(8), pages 5149-5187.
    6. Bai, Jushan & Duan, Jiangtao & Han, Xu, 2024. "The likelihood ratio test for structural changes in factor models," Journal of Econometrics, Elsevier, vol. 238(2).
    7. Conner Mullally & Jayson L Lusk, 2018. "The Impact of Farm Animal Housing Restrictions on Egg Prices, Consumer Welfare, and Production in California," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(3), pages 649-669.
    8. Verme, Paolo & Schuettler, Kirsten, 2021. "The impact of forced displacement on host communities: A review of the empirical literature in economics," Journal of Development Economics, Elsevier, vol. 150(C).
    9. Duan, Jiangtao & Bai, Jushan & Han, Xu, 2023. "Quasi-maximum likelihood estimation of break point in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 233(1), pages 209-236.
    10. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2016. "Estimation of heterogeneous panels with structural breaks," Journal of Econometrics, Elsevier, vol. 191(1), pages 176-195.
    11. Yamamoto, Yohei & Tanaka, Shinya, 2015. "Testing for factor loading structural change under common breaks," Journal of Econometrics, Elsevier, vol. 189(1), pages 187-206.
    12. Oka, Tatsushi & Perron, Pierre, 2018. "Testing for common breaks in a multiple equations system," Journal of Econometrics, Elsevier, vol. 204(1), pages 66-85.
    13. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    14. Zhongxin Ni & Xing Lu & Wenjun Xue, 2021. "Does the belt and road initiative resolve the steel overcapacity in China? Evidence from a dynamic model averaging approach," Empirical Economics, Springer, vol. 61(1), pages 279-307, July.
    15. Minyoung Jo & Sangyeol Lee, 2021. "On CUSUM test for dynamic panel models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 515-542, June.
    16. Barbora Peštová & Michal Pešta, 2017. "Change Point Estimation in Panel Data without Boundary Issue," Risks, MDPI, vol. 5(1), pages 1-22, January.
    17. Badi H. Baltagi & Chihwa Kao & Long Liu, 2017. "Estimation and identification of change points in panel models with nonstationary or stationary regressors and error term," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 85-102, March.
    18. Gorton, Gary & Metrick, Andrew & Xie, Lei, 2021. "The flight from maturity," Journal of Financial Intermediation, Elsevier, vol. 47(C).
    19. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    20. Tae-Hwy Lee & Ekaterina Seregina, 2022. "Combining Forecasts under Structural Breaks Using Graphical LASSO," Working Papers 202213, University of California at Riverside, Department of Economics.

    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:ecolet:v:134:y:2015:i:c:p:107-110. 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/ecolet .

    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.