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Bootstrap Tests of Nonnested Linear Regression Models

Author

Listed:
  • Davidson, R.
  • Mackinnon, J. G.
Abstract
Le test J applique aux modeles de regression non emboites a souvent des performances qui sont mauvaises pour la version asymptotique du test, mais tres bonnes pour la forme bootstrap. On donne une analyse theorique qui explique les deux phenomenes. On propose une version modifiee du test qui, dans sa version bootstrap, s'avere encore plus performante que le test J. Les excellentes performances des tests bootstrap sont demontrees par des experiences Monte Carlo, qui sont d'une tres grande precision grace a nos resultats theoriques, qui permettent de reduire le temps de calcul de maniere importante.

Suggested Citation

  • Davidson, R. & Mackinnon, J. G., 1995. "Bootstrap Tests of Nonnested Linear Regression Models," G.R.E.Q.A.M. 97a25, Universite Aix-Marseille III.
    • Handle: RePEc:fth:aixmeq:97a25
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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. Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
    6. McAleer, Michael, 1995. "The significance of testing empirical non-nested models," Journal of Econometrics, Elsevier, vol. 67(1), pages 149-171, May.
    7. 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.
    8. 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.
    9. 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.
    10. Davidson, Russell & MacKinnon, James G., 1999. "The Size Distortion Of Bootstrap Tests," Econometric Theory, Cambridge University Press, vol. 15(3), pages 361-376, June.
    11. 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.
    12. 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.
    13. 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.
    14. Yanqin Fan & Qi Li, 1995. "Bootstrapping J-type tests for non-nested regression models," Economics Letters, Elsevier, vol. 48(2), pages 107-112, May.
    15. 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.
    16. 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.
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    JEL classification:

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

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