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Inference via kernel smoothing of bootstrap P values

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  • Racine, Jeffrey S.
  • MacKinnon, James G.

Abstract

Resampling methods such as the bootstrap are routinely used to estimate the finite-sample null distributions of a range of test statistics. We present a simple and tractable way to perform classical hypothesis tests based upon a kernel estimate of the CDF of the bootstrap statistics. This approach has a number of appealing features: i) it can perform well when the number of bootstraps is extremely small, ii) it is approximately exact, and iii) it can yield substantial power gains relative to the conventional approach. The proposed approach is likely to be useful when the statistic being bootstrapped is computationally expensive.
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Suggested Citation

  • Racine, Jeffrey S. & MacKinnon, James G., 2007. "Inference via kernel smoothing of bootstrap P values," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5949-5957, August.
    • Handle: RePEc:eee:csdana:v:51:y:2007:i:12:p:5949-5957
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    References listed on IDEAS

    as
    1. James G. MacKinnon & Jeff Racine, 2004. "Simulation-based Tests That Can Use Any Number Of Simulations," Working Paper 1027, Economics Department, Queen's University.
    2. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.
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    Cited by:

    1. Cabane, Charlotte & Hille, Adrian & Lechner, Michael, 2015. "Mozart or Pelé? The effects of teenagers’ participation in music and sports," Economics Working Paper Series 1509, University of St. Gallen, School of Economics and Political Science.
    2. Tim Pawlowski & Ute Schüttoff & Paul Downward & Michael Lechner, 2018. "Can Sport Really Help to Meet the Millennium Development Goals? Evidence From Children in Peru," Journal of Sports Economics, , vol. 19(4), pages 498-521, May.
    3. Martin Huber & Michael Lechner & Giovanni Mellace, 2017. "Why Do Tougher Caseworkers Increase Employment? The Role of Program Assignment as a Causal Mechanism," The Review of Economics and Statistics, MIT Press, vol. 99(1), pages 180-183, March.
    4. Pawlowski, Tim & Schüttoff, Ute & Downward, Paul & Lechner, Michael, 2014. "Sport participation and Child Development in Less Developed Countries," Economics Working Paper Series 1433, University of St. Gallen, School of Economics and Political Science.
    5. Michael Lechner & Paul Downward, 2017. "Heterogeneous sports participation and labour market outcomes in England," Applied Economics, Taylor & Francis Journals, vol. 49(4), pages 335-348, January.
    6. Hugo Bodory & Lorenzo Camponovo & Martin Huber & Michael Lechner, 2020. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 183-200, January.
    7. Pawlowski, Tim & Schüttoff, Ute & Downward, Paul & Lechner, Michael, 2014. "Children’s skill formation in less developed countries – The impact of sports participation," Economics Working Paper Series 1412, University of St. Gallen, School of Economics and Political Science.
    8. Patrick Richard, 2010. "Kernel smoothing end of sample instability tests P values," Cahiers de recherche 10-19, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    9. James G. MacKinnon, 2007. "Bootstrap Hypothesis Testing," Working Paper 1127, Economics Department, Queen's University.
    10. King, Maxwell L. & Zhang, Xibin & Akram, Muhammad, 2020. "Hypothesis testing based on a vector of statistics," Journal of Econometrics, Elsevier, vol. 219(2), pages 425-455.
    11. Lechner, Michael & Hille, Adrian & Cabane, Charlotte, 2015. "Mozart or Pelé? The effects of teenagers? participation in music and sports," CEPR Discussion Papers 10556, C.E.P.R. Discussion Papers.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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

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