IDEAS home Printed from https://ideas.repec.org/p/hhs/hastef/0304.html
   My bibliography  Save this paper

Bootstrapping Error Component Models

Author

Listed:
  • Andersson, Michael K.

    (National Institute of Economic Research)

  • Karlsson, Sune

    (Dept. of Economic Statistics, Stockholm School of Economics)

Abstract

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

  • Andersson, Michael K. & Karlsson, Sune, 1999. "Bootstrapping Error Component Models," SSE/EFI Working Paper Series in Economics and Finance 304, Stockholm School of Economics, revised 30 Jun 2000.
  • Handle: RePEc:hhs:hastef:0304
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. James G. MacKinnon & Russell Davidson, 1996. "The Size And Power Of Bootstrap Tests," Working Paper 932, Economics Department, Queen's University.
    2. 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.
    3. 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.
    4. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.
    5. 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.
    6. 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.
    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. 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.
    2. Baltagi, Badi H. & Bresson, Georges & Chaturvedi, Anoop & Lacroix, Guy, 2022. "Robust Dynamic Space-Time Panel Data Models Using ?-Contamination: An Application to Crop Yields and Climate Change," IZA Discussion Papers 15815, Institute of Labor Economics (IZA).
    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. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    3. 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.
    4. Robertson, Donald & Sarafidis, Vasilis, 2015. "IV estimation of panels with factor residuals," Journal of Econometrics, Elsevier, vol. 185(2), pages 526-541.
    5. Martin A. Carree, 2002. "Nearly Unbiased Estimation in Dynamic Panel Data Models with Exogenous Variables," Tinbergen Institute Discussion Papers 02-007/2, Tinbergen Institute.
    6. 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).
    7. 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.
    8. Biørn, Erik, 2012. "The Measurement Error Problem in Dynamic Panel Data Analysis: Modeling and GMM Estimation," Memorandum 02/2012, Oslo University, Department of Economics.
    9. 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.
    10. 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.
    11. Bao, Yong & Yu, Xuewen, 2023. "Indirect inference estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1027-1053.
    12. 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.
    13. Biørn, Erik & Han, Xuehui, 2015. "Persistence, Signal-Noise Pattern and Heterogeneity in Panel Data: With an Application to the Impact of Foreign Direct Investment on GDP," Memorandum 04/2015, Oslo University, Department of Economics.
    14. Li, Hongyi & Xiao, Zhijie, 2000. "On bootstrapping regressions with unit root processes," Statistics & Probability Letters, Elsevier, vol. 48(3), pages 261-267, July.
    15. Opolska, Iweta, 2017. "The efficacy of liberalization and privatization in introducing competition into European natural gas markets," Utilities Policy, Elsevier, vol. 48(C), pages 12-21.
    16. L. G. Godfrey & C. D. Orme, 1999. "The robustness, reliabiligy and power of heteroskedasticity tests," Econometric Reviews, Taylor & Francis Journals, vol. 18(2), pages 169-194.
    17. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
    18. Ramses H. Abul Naga & Christopher Stapenhurst & Gaston Yalonetzky, 2024. "Inferring inequality: Testing for median-preserving spreads in ordinal data," Econometric Reviews, Taylor & Francis Journals, vol. 43(2-4), pages 156-174, April.
    19. 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.
    20. Yannis Psycharis & Stavroula Iliopoulou & Maria Zoi & Panagiotis Pantazis, 2021. "Beyond the socio‐economic use of fiscal transfers: The role of political factors in Greek intergovernmental grant allocations," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(3), pages 982-1008, June.

    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

    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:hhs:hastef:0304. 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: Helena Lundin (email available below). General contact details of provider: https://edirc.repec.org/data/erhhsse.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.