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Bootstrapping p Values and Power in the First-Order Autoregression: A Monte Carlo Investigation

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  • Rayner, Robert K

Abstract

The small-sample behavior of the bootstrap is investigated as a method for estimating p values and power in the stationary first-order autoregressive model. Monte Carlo methods are used to examine the bootstrap and Student-t approximations to the true distribution of the test statistic frequently used for testing hypotheses on the underlying slope parameter. In contrast to Student's t, the results suggest that the bootstrap can accurately estimate p values and power in this model in sample sizes as small as 5-10.

Suggested Citation

  • Rayner, Robert K, 1990. "Bootstrapping p Values and Power in the First-Order Autoregression: A Monte Carlo Investigation," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 251-263, April.
  • Handle: RePEc:bes:jnlbes:v:8:y:1990:i:2:p:251-63
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    Cited by:

    1. Diebold, Francis X. & Chen, Celia, 1996. "Testing structural stability with endogenous breakpoint A size comparison of analytic and bootstrap procedures," Journal of Econometrics, Elsevier, vol. 70(1), pages 221-241, January.
    2. Capps Jr., Oral & Church, Jeffrey & Alan Love, H., 2003. "Specification issues and confidence intervals in unilateral price effects analysis," Journal of Econometrics, Elsevier, vol. 113(1), pages 3-31, March.
    3. Dufour, Jean-Marie & Khalaf, Lynda, 2001. "Finite-Sample Simulation-Based Tests in Seemingly Unrelated Regressions," Cahiers de recherche 0111, Université Laval - Département d'économique.
    4. Rayner, Robert K., 1991. "Resampling methods for tests in regression models with autocorrelated errors," Economics Letters, Elsevier, vol. 36(3), pages 281-284, July.
    5. Burridge, Peter & Robert Taylor, A. M., 2004. "Bootstrapping the HEGY seasonal unit root tests," Journal of Econometrics, Elsevier, vol. 123(1), pages 67-87, November.
    6. Jeong, Jinook & Chung, Seoung, 2001. "Bootstrap tests for autocorrelation," Computational Statistics & Data Analysis, Elsevier, vol. 38(1), pages 49-69, November.

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