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Bootstrap Tests of Nonnested Linear Regression Models

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  • Davidson, Russell
  • MacKinnon, James G.

Abstract

The J test for nonnested regression models often works badly as an asypmtotic test, but it generally works very well when bootstrapped. We provide a theroretical analysis of the J test which explains both of these phenomena. We also propose a modified version of the test which works even better than the ordinary J test when bootstrapped. Using our theoretical results to make simulation much faster, we obtain extremely accurate Monte Carlo results which demonstrate just how well the bootstraped tests perform.
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Suggested Citation

  • Davidson, Russell & MacKinnon, James G., 1997. "Bootstrap Tests of Nonnested Linear Regression Models," Queen's Institute for Economic Research Discussion Papers 273388, Queen's University - Department of Economics.
    • Handle: RePEc:ags:queddp:273388
    DOI: 10.22004/ag.econ.273388
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    References listed on IDEAS

    as
    1. Davidson, Russell & MacKinnon, James G, 1981. "Several Tests for Model Specification in the Presence of Alternative Hypotheses," Econometrica, Econometric Society, vol. 49(3), pages 781-793, May.
    2. James G. MacKinnon & Russell Davidson, 1996. "The Size And Power Of Bootstrap Tests," Working Paper 932, Economics Department, Queen's University.
    3. MacKinnon, James G. & White, Halbert & Davidson, Russell, 1983. "Tests for model specification in the presence of alternative hypotheses : Some further results," Journal of Econometrics, Elsevier, vol. 21(1), pages 53-70, January.
    4. M. H. Pesaran, 1974. "On the General Problem of Model Selection," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 41(2), pages 153-171.
    5. Yanqin Fan & Qi Li, 1995. "Bootstrapping J-type tests for non-nested regression models," Economics Letters, Elsevier, vol. 48(2), pages 107-112, May.
    6. Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.
    7. Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
    8. McAleer, Michael, 1995. "The significance of testing empirical non-nested models," Journal of Econometrics, Elsevier, vol. 67(1), pages 149-171, May.
    9. Davidson, Russell & MacKinnon, James G., 1999. "The Size Distortion Of Bootstrap Tests," Econometric Theory, Cambridge University Press, vol. 15(3), pages 361-376, June.
    10. Russell Davidson & James G. Mackinnon, 1982. "Some Non-Nested Hypothesis Tests and the Relations Among Them," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 49(4), pages 551-565.
    11. Fisher, Gordon R. & McAleer, Michael, 1981. "Alternative procedures and associated tests of significance for non-nested hypotheses," Journal of Econometrics, Elsevier, vol. 16(1), pages 103-119, May.
    12. Godfrey, L. G., 1998. "Tests of non-nested regression models some results on small sample behaviour and the bootstrap," Journal of Econometrics, Elsevier, vol. 84(1), pages 59-74, May.
    13. Michelis, Leo, 1996. "The Null Distribution of Nonnested Tests with Nearly Orthogonal Regression Models," Econometric Theory, Cambridge University Press, vol. 12(05), pages 870-871, December.
    14. Godfrey, Leslie G, 1983. "Testing Non-Nested Models after Estimation by Instrumental Variables or Least Squares," Econometrica, Econometric Society, vol. 51(2), pages 355-365, March.
    15. Michelis, Leo, 1999. "The distributions of the J and Cox non-nested tests in regression models with weakly correlated regressors," Journal of Econometrics, Elsevier, vol. 93(2), pages 369-401, December.
    16. Godfrey, L. G. & Pesaran, M. H., 1983. "Tests of non-nested regression models: Small sample adjustments and Monte Carlo evidence," Journal of Econometrics, Elsevier, vol. 21(1), pages 133-154, January.
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    Keywords

    Financial Economics;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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