IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v16y2001i2d10.1007_s001800100061.html
   My bibliography  Save this article

Bootstrapping Error Component Models

Author

Listed:
  • Michael K. Andersson

    (National Institute of Economic Research)

  • Sune Karlsson

    (Stockholm School of Economics)

Abstract

Summary This paper proposes several resampling algorithms suitable for error component models and evaluates them in the context of bootstrap testing. In short, all the algorithms work well and lead to tests with correct or close to correct size. There is thus little or no reason not to use the bootstrap with error component models.

Suggested Citation

  • Michael K. Andersson & Sune Karlsson, 2001. "Bootstrapping Error Component Models," Computational Statistics, Springer, vol. 16(2), pages 221-231, July.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:2:d:10.1007_s001800100061
    DOI: 10.1007/s001800100061
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s001800100061
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s001800100061?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ziliak, James P, 1997. "Efficient Estimation with Panel Data When Instruments Are Predetermined: An Empirical Comparison of Moment-Condition Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 419-431, October.
    2. James G. MacKinnon & Russell Davidson, 1996. "The Size And Power Of Bootstrap Tests," Working Paper 932, Economics Department, Queen's University.
    3. Bellmann, L & Breitung, J & Wagner, Joachim, 1989. "Bias Correction and Bootstrapping of Error Component Models for Panel Data: Theory and Applications," Empirical Economics, Springer, vol. 14(4), pages 329-342.
    4. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.
    5. 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.
    6. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, 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. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2022. "Robust Dynamic Space-Time Panel Data Models Using ε-contamination: An Application to Crop Yields and Climate Change," Center for Policy Research Working Papers 254, Center for Policy Research, Maxwell School, Syracuse University.
    2. Baltagi, Badi H. & Bresson, Georges & Chaturvedi, Anoop & Lacroix, Guy, 2018. "Robust linear static panel data models using ε-contamination," Journal of Econometrics, Elsevier, vol. 202(1), pages 108-123.
    3. Stanislav Anatolyev, 2007. "The basics of bootstrapping (in Russian)," Quantile, Quantile, issue 3, pages 1-12, September.

    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. Martin, Michael A., 2007. "Bootstrap hypothesis testing for some common statistical problems: A critical evaluation of size and power properties," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6321-6342, August.
    2. Christian Peretti, 2007. "Long Memory and Hysteresis," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 363-389, Springer.
    3. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    4. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    5. Monfardini, Chiara, 2003. "An illustration of Cox's non-nested testing procedure for logit and probit models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 425-444, March.
    6. Robertson, Donald & Sarafidis, Vasilis, 2015. "IV estimation of panels with factor residuals," Journal of Econometrics, Elsevier, vol. 185(2), pages 526-541.
    7. Assi Okara, 2022. "Building Stronger Economic Institutions in Developing Countries, the Role of FDI," CERDI Working papers hal-03617915, HAL.
    8. Martin A. Carree, 2002. "Nearly Unbiased Estimation in Dynamic Panel Data Models with Exogenous Variables," Tinbergen Institute Discussion Papers 02-007/2, Tinbergen Institute.
    9. Beck, Tobias, 2021. "How the honesty oath works: Quick, intuitive truth telling under oath," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 94(C).
    10. Norman Loayza & Klaus Schmidt-Hebbel & Luis Servén, 2000. "What Drives Private Saving Across the World?," The Review of Economics and Statistics, MIT Press, vol. 82(2), pages 165-181, May.
    11. Ignace De Vos & Gerdie Everaert & Ilse Ruyssen, 2015. "Bootstrap-based bias correction and inference for dynamic panels with fixed effects," Stata Journal, StataCorp LLC, vol. 15(4), pages 986-1018, December.
    12. Ernest Dautovic, 2017. "The effect of real-time fiscal policy on sovereign interest rates in OECD countries," International Economics and Economic Policy, Springer, vol. 14(1), pages 167-185, January.
    13. M. E. Bontempi & I. Mammi, 2014. "pca2: implementing a strategy to reduce the instrument count in panel GMM," Working Papers wp960, Dipartimento Scienze Economiche, Universita' di Bologna.
    14. repec:hal:spmain:info:hdl:2441/2041 is not listed on IDEAS
    15. Biørn, Erik, 2012. "The Measurement Error Problem in Dynamic Panel Data Analysis: Modeling and GMM Estimation," Memorandum 02/2012, Oslo University, Department of Economics.
    16. Harris, R. I. D. & Judge, G., 1998. "Small sample testing for cointegration using the bootstrap approach," Economics Letters, Elsevier, vol. 58(1), pages 31-37, January.
    17. Badi H. Baltagi, 2021. "Dynamic Panel Data Models," Springer Texts in Business and Economics, in: Econometric Analysis of Panel Data, edition 6, chapter 0, pages 187-228, Springer.
    18. Thomas Gries & Margarete Redlin, 2008. "International Integration and Regional Development in China," WIDER Working Paper Series RP2008-66, World Institute for Development Economic Research (UNU-WIDER).
    19. Florian Pelgrin & Arnaud Sylvain & Eric Heyer, 2004. "Capital operating time and working time in the production function : an evaluation on a panel firms over the period 1989-2001," SciencePo Working papers Main hal-00972838, HAL.
    20. Bao, Yong & Yu, Xuewen, 2023. "Indirect inference estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1027-1053.
    21. Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.

    More about this item

    Keywords

    Panel data; Bootstrap; Bootstrap test; Resampling;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    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:spr:compst:v:16:y:2001:i:2:d:10.1007_s001800100061. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.