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Post-simulation Analysis of Monte Carlo Experiments: Interpreting Pesaran's (1974) Study of Non-nested Hypothesis Test Statistics

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  • Neil R. Ericsson

Abstract

"Monte Carlo experimentation in econometrics helps ‘solve’ deterministic problems by simulating stochastic analogues in which the analytical unknowns are reformulated as parameters to be estimated." (Hendry (1980)) With that in mind, Monte Carlo studies may be divided operationally into three phases: design, simulation, and post-simulation analysis. This paper provides a guide to the last of those three, post-simulation analysis, given the design and simulation of a Monte Carlo study, and uses Pesaran's (1974) study of statistics for testing non-nested hypotheses to illustrate the techniques described. A statistic is derived for testing for significant deviations between the asymptotic and (observed) finite sample properties. Further, that statistic provides the basis for analysing discrepancies between the finite sample and asymptotic properties using response surfaces. The results for Pesaran's study indicate the value of asymptotic theory in interpreting finite sample properties and certain limitations for doing so. Finally, a method is proposed for adjusting the finite sample sizes of different test statistics so that comparisons of their power may be made. Extensions to other finite sample properties are indicated.

Suggested Citation

  • Neil R. Ericsson, 1986. "Post-simulation Analysis of Monte Carlo Experiments: Interpreting Pesaran's (1974) Study of Non-nested Hypothesis Test Statistics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 691-707.
    • Handle: RePEc:oup:restud:v:53:y:1986:i:4:p:691-707.
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    Cited by:

    1. Harvey, David I. & van Dijk, Dick, 2006. "Sample size, lag order and critical values of seasonal unit root tests," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2734-2751, June.
    2. repec:jss:jstsof:32:i02 is not listed on IDEAS
    3. Levine, Ross, 1989. "The pricing of forward exchange rates," Journal of International Money and Finance, Elsevier, vol. 8(2), pages 163-179, June.
    4. McAleer, Michael, 1995. "The significance of testing empirical non-nested models," Journal of Econometrics, Elsevier, vol. 67(1), pages 149-171, May.
    5. Neil R. Ericsson, 1987. "Monte Carlo methodology and the finite sample properties of statistics for testing nested and non-nested hypotheses," International Finance Discussion Papers 317, Board of Governors of the Federal Reserve System (U.S.).
    6. Neil R. Ericsson, 1986. "Post-simulation Analysis of Monte Carlo Experiments: Interpreting Pesaran's (1974) Study of Non-nested Hypothesis Test Statistics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 691-707.
    7. de Brouwer, Gordon & Ericsson, Neil R, 1998. "Modeling Inflation in Australia," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 433-449, October.
    8. BHATTI, M.Ishaq & BODLA, Mahmud, A., 2008. "Empirical Power Comparison Of Non-Nested Tests For The Evm: Some Monte Carlo Evidence," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 5(2).

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