IDEAS home Printed from https://ideas.repec.org/a/cup/etheor/v19y2003i06p1128-1143_19.html
   My bibliography  Save this article

Critical Values And P Values Of Bessel Process Distributions: Computation And Application To Structural Break Tests

Author

Listed:
  • Estrella, Arturo

Abstract

The p values of structural break tests, when the break date or dates are unknown, must be calculated in terms of the probability distributions of functions of Bessel processes. The literature so far has maintained that direct computation of these p values and of the corresponding critical values is too difficult and has relied on approximations based on simulations, asymptotic expansions, or curve fitting. This paper presents a fast simple method of calculating exact p values and critical values and uses the method to evaluate the accuracy of the various approximations.The author is grateful for comments and suggestions from Don Andrews, Clint Cummins, Jeff Fuhrer, Ken Garbade, Jim Mahoney, Tony Rodrigues, Josh Rosenberg, Sebastian Schich, participants in a workshop at the Federal Reserve Bank of New York, and the referees. The views expressed in this paper are those of the author and do not necessarily represent those of the Federal Reserve Bank of New York or the Federal Reserve System.

Suggested Citation

  • Estrella, Arturo, 2003. "Critical Values And P Values Of Bessel Process Distributions: Computation And Application To Structural Break Tests," Econometric Theory, Cambridge University Press, vol. 19(6), pages 1128-1143, December.
    • Handle: RePEc:cup:etheor:v:19:y:2003:i:06:p:1128-1143_19
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0266466603196107/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Tom Campbell & Nathaniel T. Wilcox, 2020. "Younger Federal District Court Judges Favor Presidential Power," Journal of Law and Economics, University of Chicago Press, vol. 63(1), pages 181-202.
    2. Rosa, Carlo, 2011. "Words that shake traders," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 915-934.
    3. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," Journal of Econometrics, Elsevier, vol. 196(2), pages 259-274.
    4. Castro, Carlos & Ferrari, Stijn, 2014. "Measuring and testing for the systemically important financial institutions," Journal of Empirical Finance, Elsevier, vol. 25(C), pages 1-14.
    5. Li, Kunpeng, 2018. "Spatial panel data models with structural change," MPRA Paper 85388, University Library of Munich, Germany.
    6. Paruolo, Paolo, 2006. "Common trends and cycles in I(2) VAR systems," Journal of Econometrics, Elsevier, vol. 132(1), pages 143-168, May.
    7. Piterbarg, Vladimir I. & Rodionov, Igor V., 2020. "High excursions of Bessel and related random processes," Stochastic Processes and their Applications, Elsevier, vol. 130(8), pages 4859-4872.
    8. Heikki Kauppi, 2008. "Yield-Curve Based Probit Models for Forecasting U.S. Recessions: Stability and Dynamics," Discussion Papers 31, Aboa Centre for Economics.
    9. Gonzalo, Jesus & Pitarakis, Jean-Yves, 2010. "Regime specific predictability in predictive regressions," Discussion Paper Series In Economics And Econometrics 0916, Economics Division, School of Social Sciences, University of Southampton.
    10. Charles-Elie Rabier & Jean-Marc Azaïs & Jean-Michel Elsen & Céline Delmas, 2019. "Chi-square processes for gene mapping in a population with family structure," Statistical Papers, Springer, vol. 60(1), pages 239-271, February.
    11. Javier Hidalgo & Marcia M Schafgans, 2015. "Inference and Testing Breaks in Large Dynamic Panels with Strong Cross Sectional Dependence," STICERD - Econometrics Paper Series /2015/583, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    12. Ng, Eric C.Y., 2012. "Forecasting US recessions with various risk factors and dynamic probit models," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 112-125.
    13. Sandip Sinharay, 2016. "Person Fit Analysis in Computerized Adaptive Testing Using Tests for a Change Point," Journal of Educational and Behavioral Statistics, , vol. 41(5), pages 521-549, October.
    14. A. Batsidis & N. Martín & L. Pardo & K. Zografos, 2016. "ϕ-Divergence Based Procedure for Parametric Change-Point Problems," Methodology and Computing in Applied Probability, Springer, vol. 18(1), pages 21-35, March.
    15. Jesús Gonzalo & Jean-Yves Pitarakis, 2011. "Regime-Specific Predictability in Predictive Regressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 229-241, June.
    16. Christiansen, Charlotte & Eriksen, Jonas Nygaard & Møller, Stig Vinther, 2014. "Forecasting US recessions: The role of sentiment," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 459-468.
    17. Kim, Dukpa & Perron, Pierre, 2009. "Assessing the relative power of structural break tests using a framework based on the approximate Bahadur slope," Journal of Econometrics, Elsevier, vol. 149(1), pages 26-51, April.
    18. Omtzigt, Pieter & Paruolo, Paolo, 2005. "Impact factors," Journal of Econometrics, Elsevier, vol. 128(1), pages 31-68, September.
    19. K. B. S. Huth & L. J. Waldorp & J. Luigjes & A. E. Goudriaan & R. J. Holst & M. Marsman, 2022. "A Note on the Structural Change Test in Highly Parameterized Psychometric Models," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1064-1080, September.
    20. 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.
    21. Christis Katsouris, 2023. "Predictability Tests Robust against Parameter Instability," Papers 2307.15151, arXiv.org.
    22. Matthew Davis & Fernando V. Ferreira, 2017. "Housing Disease and Public School Finances," NBER Working Papers 24140, National Bureau of Economic Research, Inc.
    23. Arturo Estrella & Anthony P. Rodrigues, 2005. "One-sided test for an unknown breakpoint: theory, computation, and application to monetary theory," Staff Reports 232, Federal Reserve Bank of New York.
    24. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," LSE Research Online Documents on Economics 68839, London School of Economics and Political Science, LSE Library.
    25. Davis, Matthew & Ferreira, Fernando, 2022. "Housing disease and public school finances," Economics of Education Review, Elsevier, vol. 88(C).

    More about this item

    Statistics

    Access and download statistics

    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:cup:etheor:v:19:y:2003:i:06:p:1128-1143_19. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/ect .

    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.