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Simulation-based Tests that Can Use Any Number of Simulations

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Author Info

  • Jeff Racine

    ()
    (McMaster University)

  • James G. MacKinnon

    ()
    (Queen's University)

Abstract

Conventional procedures for Monte Carlo and bootstrap tests require that B, the number of simulations, satisfy a specific relationship with the level of the test. Otherwise, a test that would instead be exact will either overreject or underreject for finite B. We present expressions for the rejection frequencies associated with existing procedures and propose a new procedure that yields exact Monte Carlo tests for any positive value of B. This procedure, which can also be used for bootstrap tests, is likely to be most useful when simulation is expensive.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1027.pdf
File Function: First version 2004
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Bibliographic Info

Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1027.

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Length: 12 pages
Date of creation: Oct 2004
Date of revision:
Handle: RePEc:qed:wpaper:1027

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Related research

Keywords: resampling; Monte Carlo test; bootstrap test; percentiles; simulation;

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References

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  1. 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. JAMES G. MacKINNON, 2006. "Bootstrap Methods in Econometrics," The Economic Record, The Economic Society of Australia, vol. 82(s1), pages S2-S18, 09.
  2. Jeff Racine & James G. MacKinnon, 2006. "Inference via kernel smoothing of bootstrap P values," Working Papers 1054, Queen's University, Department of Economics.
  3. James G. MacKinnon, 2012. "Thirty Years of Heteroskedasticity-Robust Inference," Working Papers 1268, Queen's University, Department of Economics.
  4. James G. MacKinnon, 2007. "Bootstrap Hypothesis Testing," Working Papers 1127, Queen's University, Department of Economics.

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