IDEAS home Printed from https://ideas.repec.org/a/vrs/stintr/v19y2018i1p7-23n8.html
   My bibliography  Save this article

A Bayesian Inference Of Multiple Structural Breaks In Mean And Error Variance In Panelar (1) Model

Author

Listed:
  • Agiwal Varun

    (Department of Statistics, Central University of Rajasthan, Bandersindri, Ajmer, India)

  • Kumar Jitendra

    (Department of Statistics, Central University of Rajasthan, Bandersindri, Ajmer, India)

  • Shangodoyin Dahud Kehinde

    (Department of Statistics, University of Botswana, Gaborone, Botswana)

Abstract

This paper explores the effect of multiple structural breaks to estimate the parameters and test the unit root hypothesis in panel data time series model under Bayesian perspective. These breaks are present in both mean and error variance at the same time point. We obtain Bayes estimates for different loss function using conditional posterior distribution, which is not coming in a closed form, and this is approximately explained by Gibbs sampling. For hypothesis testing, posterior odds ratio is calculated and solved via Monte Carlo Integration. The proposed methodology is illustrated with numerical examples.

Suggested Citation

  • Agiwal Varun & Kumar Jitendra & Shangodoyin Dahud Kehinde, 2018. "A Bayesian Inference Of Multiple Structural Breaks In Mean And Error Variance In Panelar (1) Model," Statistics in Transition New Series, Polish Statistical Association, vol. 19(1), pages 7-23, March.
    • Handle: RePEc:vrs:stintr:v:19:y:2018:i:1:p:7-23:n:8
    DOI: 10.21307/stattrans-2018-001
    as

    Download full text from publisher

    File URL: https://doi.org/10.21307/stattrans-2018-001
    Download Restriction: no
    File URL: https://libkey.io/10.21307/stattrans-2018-001?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. 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.
    2. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    3. Philip Preuss & Ruprecht Puchstein & Holger Dette, 2015. "Detection of Multiple Structural Breaks in Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 654-668, June.
    4. Alexander Aue & Lajos Horváth, 2013. "Structural breaks in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(1), pages 1-16, January.
    5. Xiao-Ming Li, 2004. "A Quasi-Bayesian Analysis of Structural Breaks: China's Output and Productivity Series," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 3(1), pages 57-65, April.
    6. Dandan Liu & Rui Li & Zijun Wang, 2011. "Testing for structural breaks in panel varying coefficient models: with an application to OECD health expenditure," Empirical Economics, Springer, vol. 40(1), pages 95-118, February.
    7. Wang, Jiahui & Zivot, Eric, 2000. "A Bayesian Time Series Model of Multiple Structural Changes in Level, Trend, and Variance," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 374-386, July.
    8. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    9. Yunjong Eo & James Morley, 2015. "Likelihood‐ratio‐based confidence sets for the timing of structural breaks," Quantitative Economics, Econometric Society, vol. 6(2), pages 463-497, July.
    10. Sugita, Katsuhiro & 杉田, 勝弘, 2006. "Bayesian Analysis of Dynamic Multivariate Models with Multiple Structural Breaks," Discussion Papers 2006-14, Graduate School of Economics, Hitotsubashi University.
    11. Altissimo, Filippo & Corradi, Valentina, 2003. "Strong rules for detecting the number of breaks in a time series," Journal of Econometrics, Elsevier, vol. 117(2), pages 207-244, December.
    12. Phillips, P C B, 1991. "To Criticize the Critics: An Objective Bayesian Analysis of Stochastic Trends," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 333-364, Oct.-Dec..
    13. Geweke, John & Jiang, Yu, 2011. "Inference and prediction in a multiple-structural-break model," Journal of Econometrics, Elsevier, vol. 163(2), pages 172-185, August.
    14. Degui Li & Junhui Qian & Liangjun Su, 2016. "Panel Data Models With Interactive Fixed Effects and Multiple Structural Breaks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1804-1819, October.
    15. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    16. Meligkotsidou, Loukia & Tzavalis, Elias & Vrontos, Ioannis, 2017. "On Bayesian analysis and unit root testing for autoregressive models in the presence of multiple structural breaks," Econometrics and Statistics, Elsevier, vol. 4(C), pages 70-90.
    17. Eo, Yunjong, 2012. "Bayesian Inference about the Types of Structural Breaks When There are Many Breaks," Working Papers 2012-05, University of Sydney, School of Economics.
    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. Varun Agiwal & Jitendra Kumar & Dahud Kehinde Shangodoyin, 2018. "A Bayesian Inference Of Multiple Structural Breaks In Mean And Error Variance In Panel Ar (1) Model," Statistics in Transition New Series, Polish Statistical Association, vol. 19(1), pages 7-23, March.
    2. 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.
    3. Varun Agiwal & Jitendra Kumar & Dahud Kehinde Shangodoyin, 2020. "A Bayesian analysis of complete multiple breaks in a panel autoregressive (CMB-PAR(1)) time series model," Statistics in Transition New Series, Polish Statistical Association, vol. 21(5), pages 133-149, December.
    4. Chen, Likai & Wang, Weining & Wu, Wei Biao, 2019. "Inference of Break-Points in High-Dimensional Time Series," IRTG 1792 Discussion Papers 2019-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    6. Valerio Filoso, Valerio & Panico, Carlo & Papagni, Erasmo & Francesco, Purificato & Vázquez Suarez, Marta, 2016. "Causes and timing of the European debt crisis: An econometric evaluation," MPRA Paper 75847, University Library of Munich, Germany.
    7. Kleiber, Christian, 2016. "Structural Change in (Economic) Time Series," Working papers 2016/06, Faculty of Business and Economics - University of Basel.
    8. Ma, Shujie & Su, Liangjun, 2018. "Estimation of large dimensional factor models with an unknown number of breaks," Journal of Econometrics, Elsevier, vol. 207(1), pages 1-29.
    9. Zifeng Zhao & Feiyu Jiang & Xiaofeng Shao, 2022. "Segmenting time series via self‐normalisation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1699-1725, November.
    10. Otilia Boldea & Bettina Drepper & Zhuojiong Gan, 2020. "Change point estimation in panel data with time‐varying individual effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 712-727, September.
    11. Yazgan, M. Ege & Özkan, Harun, 2015. "Detecting structural changes using wavelets," Finance Research Letters, Elsevier, vol. 12(C), pages 23-37.
    12. Meligkotsidou, Loukia & Tzavalis, Elias & Vrontos, Ioannis, 2017. "On Bayesian analysis and unit root testing for autoregressive models in the presence of multiple structural breaks," Econometrics and Statistics, Elsevier, vol. 4(C), pages 70-90.
    13. Alastair R. Hall & Denise R. Osborn & Nikolaos Sakkas, 2017. "The asymptotic behaviour of the residual sum of squares in models with multiple break points," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 667-698, October.
    14. Chen, Sanpan & Cui, Guowei & Zhang, Jianhua, 2017. "On testing for structural break of coefficients in factor-augmented regression models," Economics Letters, Elsevier, vol. 161(C), pages 141-145.
    15. Hännikäinen Jari, 2017. "Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-22, January.
    16. Mohitosh Kejriwal, 2020. "A Robust Sequential Procedure for Estimating the Number of Structural Changes in Persistence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(3), pages 669-685, June.
    17. James Nolan & Zoe Laulederkind, 2022. "Plane to See? Empirical Analysis of the 1999–2006 Air Cargo Cartel," Advances in Airline Economics, in: The International Air Cargo Industry, volume 9, pages 241-262, Emerald Group Publishing Limited.
    18. Amey Sapre, 2014. "Madhya Pradesh: Does Agriculture Determine the State’s Growth Trajectory?," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 8(1), pages 39-57, February.
    19. Rebeca Jimenez-Rodriguez & Amalia Morales-Zumaquero & Balazs Egert, 2010. "The VARying Effect of Foreign Shocks in Central and Eastern Europe," William Davidson Institute Working Papers Series wp989, William Davidson Institute at the University of Michigan.
    20. Yunjong Eo & James Morley, 2022. "Why Has the U.S. Economy Stagnated since the Great Recession?," The Review of Economics and Statistics, MIT Press, vol. 104(2), pages 246-258, May.

    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:vrs:stintr:v:19:y:2018:i:1:p:7-23:n:8. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.