Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/sce/scecfa/304.html
   My bibliography  Save this paper

Graphical Methods for Investigating the Finite-sample Properties of Confidence Regions: an application to long memory

Author

Listed:
  • Christian de Peretti

    (Department of Economics University of Evry-Val-d'Essonne (France).)

  • Carole Siani

    (University of Claude Bernard Lyon 1 (France).)

Abstract
In the literature, there are not satisfactory methods for measuring and presenting the performance of confidence regions. In this paper, techniques for measuring effectiveness of confidence regions and for the graphical display of simulation evidence concerning the coverage and effectiveness of confidence regions are developed and illustrated. Three types of figures are discussed: called coverage plots, coverage discrepancy plots, and coverage effectiveness curves, that permits to show the ``true'' effectiveness, rather than a spurious nominal effectiveness. We demonstrate that when simulations are run to compute the coverage for only one confidence level, which is done for classical presentations in tables, all the information useful for building the coverage plot is present. Thus, there is absolutely no loss of computing time by using this method. These figures are used to illustrate the finite sample properties of long range dependence confidence regions. Particularly, we present and comment classical confidence intervals and confidence intervals based on inverting bootstrap tests for the long range dependence parameter in the ARFIMA models. Monte Carlo results on these confidence intervals for various situations are also presented. We show that classical confidence intervals have very poor performances, even the percentile-t interval, whereas confidence intervals based on inverting bootstrap tests have quite satisfactory performance. These intervals are then applied on the S&P500 index to illustrate a realistic case

Suggested Citation

  • Christian de Peretti & Carole Siani, 2006. "Graphical Methods for Investigating the Finite-sample Properties of Confidence Regions: an application to long memory," Computing in Economics and Finance 2006 304, Society for Computational Economics.
    • Handle: RePEc:sce:scecfa:304
    as

    Download full text from publisher

    File URL: http://repec.org/sce2006/up.30150.1141069252.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
    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. repec:ebl:ecbull:v:3:y:2008:i:5:p:1-7 is not listed on IDEAS
    2. James E. Prieger, "undated". "Conditional Moment Tests for Parametric Duration Models," Department of Economics 00-10, California Davis - Department of Economics.
    3. Godfrey, L. G. & Veall, M. R., 1998. "Bootstrap-based critical values for tests of common factor restrictions," Economics Letters, Elsevier, vol. 59(1), pages 1-5, April.
    4. Nyakabawo, Wendy & Miller, Stephen M. & Balcilar, Mehmet & Das, Sonali & Gupta, Rangan, 2015. "Temporal causality between house prices and output in the US: A bootstrap rolling-window approach," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 55-73.
    5. Wanling Huang & Artem Prokhorov, 2014. "A Goodness-of-fit Test for Copulas," Econometric Reviews, Taylor & Francis Journals, vol. 33(7), pages 751-771, October.
    6. Shakoor Ahmed & Khorshed Alam & Afzalur Rashid & Jeff Gow, 2020. "Militarisation, Energy Consumption, CO2 Emissions and Economic Growth in Myanmar," Defence and Peace Economics, Taylor & Francis Journals, vol. 31(6), pages 615-641, August.
    7. Emmanuel Flachaire, 2000. "Les méthodes du bootstrap dans les modèles de régression," Économie et Prévision, Programme National Persée, vol. 142(1), pages 183-194.
    8. Chesher, Andrew & Dhaene, Geert & Gouriéroux, Christian & Scaillet, Olivier, 1999. "Bartlett Identities Tests," LIDAM Discussion Papers IRES 1999019, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    9. Fuchun Li & Greg Tkacz, 2001. "A Consistent Bootstrap Test for Conditional Density Functions with Time-Dependent Data," Staff Working Papers 01-21, Bank of Canada.
    10. Murphy, Anthony, 2007. "Score tests of normality in bivariate probit models," Economics Letters, Elsevier, vol. 95(3), pages 374-379, June.
    11. Yazid Dissou & Reza Ghazal, 2010. "Energy Substitutability in Canadian Manufacturing Econometric Estimation with Bootstrap Confidence Intervals," The Energy Journal, , vol. 31(1), pages 121-148, January.
    12. Lukasz Lach, 2010. "Application of Bootstrap Methods in Investigation of Size of the Granger Causality Test for Integrated VAR Systems," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 8(2), pages 167-186.
    13. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2014. "Housing and the Great Depression," Applied Economics, Taylor & Francis Journals, vol. 46(24), pages 2966-2981, August.
    14. Geert Dhaene & J. M. C. Santos Silva, 2012. "Specification and testing of models estimated by quadrature," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 322-332, March.
    15. Davidson, Russell & MacKinnon, James G., 1999. "The Size Distortion Of Bootstrap Tests," Econometric Theory, Cambridge University Press, vol. 15(3), pages 361-376, June.
    16. Flachaire, Emmanuel, 1999. "A better way to bootstrap pairs," Economics Letters, Elsevier, vol. 64(3), pages 257-262, September.
    17. Roula Inglesi-Lotz & Mehmet Balcilar & Rangan Gupta, 2014. "Time-varying causality between research output and economic growth in US," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 203-216, July.
    18. James G. MacKinnon & Russell Davidson, 1996. "The Size And Power Of Bootstrap Tests," Working Paper 932, Economics Department, Queen's University.
    19. Narayan, Paresh Kumar & Prasad, Arti, 2008. "Electricity consumption-real GDP causality nexus: Evidence from a bootstrapped causality test for 30 OECD countries," Energy Policy, Elsevier, vol. 36(2), pages 910-918, February.
    20. Boldea, Otilia & Magnus, Jan R., 2009. "Maximum Likelihood Estimation of the Multivariate Normal Mixture Model," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1539-1549.
    21. Maxwell L. King & Xibin Zhang & Muhammad Akram, 2011. "A New Procedure For Multiple Testing Of Econometric Models," Monash Econometrics and Business Statistics Working Papers 7/11, Monash University, Department of Econometrics and Business Statistics.

    More about this item

    Keywords

    Graphical method; confidence region; long memory; double bootstrap; inverting tests;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:sce:scecfa:304. 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: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.html .

    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.