IDEAS home Printed from https://ideas.repec.org/p/ags/queddp/273347.html
   My bibliography  Save this paper

The Size Distortion of Bootstrap Tests

Author

Listed:
  • Davidson, Russell
  • MacKinnon, James G.

Abstract

Bootstrap tests are tests for which the signicance level is calculated by some sort of bootstrap procedure which may be parametric or nonparametric We provide a theoretical framework in which to study the size distortions of bootstrap P values We show that in many circumstances the size distortion of a bootstrap test will be one whole order of magnitude smaller than that of the corresponding asymptotic test We also show that at least in the parametric case the magnitude of the distortion will depend on the shape of what we call the P value function Monte Carlo results are presented for the case of nonnested hypothesis tests These results conrm and illustrate the utility of our theoretical results and they also suggest that bootstrap tests may often work extremely well in practice This research was supported in part by grants from the Social Sciences and Humanities Research Council of Canada An earlier version was presented at Universidad Carlos III de Madrid Universidad Complutense de Madrid Cambridge University INSEECREST Paris CORE LouvainlaNeuve the Tinbergen Institute Amsterdam the University of Geneva the European University Institute Florence the ESRC Econometrics Conference Bristol and the Berkeley Symposium on the Bootstrap We are grateful to many seminar participants and to two anonymous referees for comments on the earlier version We are especially grateful to Joel Horowitz not only for comments but also for his probing questions that led us to clarify the paper The paper was written while the second author was visiting GREQAM

Suggested Citation

  • Davidson, Russell & MacKinnon, James G., 1996. "The Size Distortion of Bootstrap Tests," Queen's Institute for Economic Research Discussion Papers 273347, Queen's University - Department of Economics.
    • Handle: RePEc:ags:queddp:273347
    DOI: 10.22004/ag.econ.273347
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/273347/files/qed_wp_936.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.273347?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Davidson, Russell & MacKinnon, James G, 1987. "Implicit Alternatives and the Local Power of Test Statistics," Econometrica, Econometric Society, vol. 55(6), pages 1305-1329, November.
    2. Davidson, Russell & MacKinnon, James G., 1992. "Regression-based methods for using control variates in Monte Carlo experiments," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 203-222.
    3. Davidson, Russell & MacKinnon, James G, 1981. "Several Tests for Model Specification in the Presence of Alternative Hypotheses," Econometrica, Econometric Society, vol. 49(3), pages 781-793, May.
    4. MacKinnon, James G. & White, Halbert & Davidson, Russell, 1983. "Tests for model specification in the presence of alternative hypotheses : Some further results," Journal of Econometrics, Elsevier, vol. 21(1), pages 53-70, January.
    5. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    6. Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
    7. Horowitz, J., 1996. "Bootstrap Critical Values For Tests Based On The Smoothed Maximum Score Estimator," SFB 373 Discussion Papers 1996,44, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    8. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July.
    9. Godfrey, L. G. & Pesaran, M. H., 1983. "Tests of non-nested regression models: Small sample adjustments and Monte Carlo evidence," Journal of Econometrics, Elsevier, vol. 21(1), pages 133-154, January.
    10. Joel L. Horowitz, 1996. "Bootstrap Critical Values for Tests Based on the Smoothed Maximum Score Estimator," Econometrics 9603003, University Library of Munich, Germany.
    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. James G. MacKinnon & Russell Davidson, 1996. "The Size And Power Of Bootstrap Tests," Working Paper 932, Economics Department, Queen's University.
    2. Davidson, Russell & MacKinnon, James G., 1996. "The Power of Bootstrap Tests," Queen's Institute for Economic Research Discussion Papers 273372, Queen's University - Department of Economics.
    3. Davidson, Russell & MacKinnon, James G., 2002. "Bootstrap J tests of nonnested linear regression models," Journal of Econometrics, Elsevier, vol. 109(1), pages 167-193, July.
    4. 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.
    5. Davidson, Russell & MacKinnon, James G., 1997. "Bootstrap Testing How Many Bootstraps," Queen's Institute for Economic Research Discussion Papers 273385, Queen's University - Department of Economics.
    6. Joel L. Horowitz, 2018. "Bootstrap Methods in Econometrics," Papers 1809.04016, arXiv.org.
    7. Zhenlin Yang, 2018. "Bootstrap LM tests for higher-order spatial effects in spatial linear regression models," Empirical Economics, Springer, vol. 55(1), pages 35-68, August.
    8. Bergström, Pål, 1999. "Bootstrap Methods and Applications in Econometrics - A Brief Survey," Working Paper Series 1999:2, Uppsala University, Department of Economics.
    9. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-146, March.
    10. Yang, Zhenlin, 2015. "LM tests of spatial dependence based on bootstrap critical values," Journal of Econometrics, Elsevier, vol. 185(1), pages 33-59.
    11. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    12. Jeremy Berkowitz & Lutz Kilian, 2000. "Recent developments in bootstrapping time series," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 1-48.
    13. Groneck, Max & Ludwig, Alexander & Zimper, Alexander, 2016. "A life-cycle model with ambiguous survival beliefs," Journal of Economic Theory, Elsevier, vol. 162(C), pages 137-180.
    14. Kundhi, Gubhinder & Rilstone, Paul, 2012. "Edgeworth expansions for GEL estimators," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 118-146.
    15. McAleer, Michael, 1995. "The significance of testing empirical non-nested models," Journal of Econometrics, Elsevier, vol. 67(1), pages 149-171, May.
    16. Nicolas Roys, 2016. "Persistence of Shocks and the Reallocation of Labor," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 22, pages 109-130, October.
    17. Ilaria Piatti & Fabio Trojani, 2020. "Dividend Growth Predictability and the Price–Dividend Ratio," Management Science, INFORMS, vol. 66(1), pages 130-158, January.
    18. Russell Davidson & Victoria Zinde‐Walsh, 2017. "Advances in specification testing," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(5), pages 1595-1631, December.
    19. Choi, Jaedo & Moon, Hyungsik Roger & Cho, Jin Seo, 2024. "Sequentially Estimating The Structural Equation By Power Transformation," Econometric Theory, Cambridge University Press, vol. 40(1), pages 98-161, February.
    20. La Vecchia, Davide & Moor, Alban & Scaillet, Olivier, 2023. "A higher-order correct fast moving-average bootstrap for dependent data," Journal of Econometrics, Elsevier, vol. 235(1), pages 65-81.

    More about this item

    Keywords

    Financial Economics; Research Methods/ Statistical Methods;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other

    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:ags:queddp:273347. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/qedquca.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.