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Simulation-based Tests that can Use Any Number of Simulations

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  • Racine, Jeff
  • MacKinnon, James

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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Suggested Citation

  • Racine, Jeff & MacKinnon, James, 2004. "Simulation-based Tests that can Use Any Number of Simulations," Queen's Economics Department Working Papers 273465, Queen's University - Department of Economics.
    • Handle: RePEc:ags:quedwp:273465
    DOI: 10.22004/ag.econ.273465
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    References listed on IDEAS

    as
    1. Davidson, Russell & MacKinnon, James, 2001. "Bootstrap Tests: How Many Bootstraps?," Queen's Economics Department Working Papers 273506, Queen's University - Department of Economics.
    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. Racine, Jeff & MacKinnon, James, 2006. "Inference via Kernel Smoothing of Bootstrap P Values," Queen's Economics Department Working Papers 273530, Queen's University - Department of Economics.
    2. MacKinnon, James G., 2011. "Thirty Years of Heteroskedasticity-Robust Inference," Queen's Economics Department Working Papers 273816, Queen's University - Department of Economics.
    3. Rand R. Wilcox, 2018. "Robust regression: an inferential method for determining which independent variables are most important," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 100-111, January.
    4. Rand Wilcox & Florence Clark, 2014. "Comparing robust regression lines associated with two dependent groups when there is heteroscedasticity," Computational Statistics, Springer, vol. 29(5), pages 1175-1186, October.
    5. MacKinnon, James, 2007. "Bootstrap Hypothesis Testing," Queen's Economics Department Working Papers 273603, Queen's University - Department of Economics.
    6. 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.
    7. JAMES G. MacKINNON, 2006. "Bootstrap Methods in Econometrics," The Economic Record, The Economic Society of Australia, vol. 82(s1), pages 2-18, September.
    8. MacKinnon, James G., 2011. "Thirty Years of Heteroskedasticity-Robust Inference," Queen's Economics Department Working Papers 273816, Queen's University - Department of Economics.
    9. Francisco J. Ruge-Murcia, 2013. "Generalized Method of Moments estimation of DSGE models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 20, pages 464-485, Edward Elgar Publishing.

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    Keywords

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    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

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