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Bootstrap Testing in Nonlinear Models

Author

Listed:
  • Davidson, R.
  • Mackinnon, J.G.

Abstract

When a model is nonlinear, boostrap testing can be expensive because of the need to perform at least one nonlinear estimation for every bootstrap sample. We show that it may be possible to reduce computational costs by performing only a fixed, small number of Newton steps or artificial regressions for each bootstrap sample.

Suggested Citation

  • Davidson, R. & Mackinnon, J.G., 1997. "Bootstrap Testing in Nonlinear Models," G.R.E.Q.A.M. 97a39, Universite Aix-Marseille III.
    • Handle: RePEc:fth:aixmeq:97a39
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    References listed on IDEAS

    as
    1. Gregory, Allan W & Veall, Michael R, 1987. "Formulating Wald Tests of the Restrictions Implied by the Rational Expectations Hypothesis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 2(1), pages 61-68, January.
    2. Orme, Chris, 1995. "On the Use of Artificial Regressions in Certain Microeconometric Models," Econometric Theory, Cambridge University Press, vol. 11(2), pages 290-305, February.
    3. Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
    4. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
    5. Phillips, Peter C B & Park, Joon Y, 1988. "On the Formulation of Wald Tests of Nonlinear Restrictions," Econometrica, Econometric Society, vol. 56(5), pages 1065-1083, September.
    6. Gregory, Allan W & Veall, Michael R, 1985. "Formulating Wald Tests of Nonlinear Restrictions," Econometrica, Econometric Society, vol. 53(6), pages 1465-1468, November.
    7. Olsen, Randall J, 1978. "Note on the Uniqueness of the Maximum Likelihood Estimator for the Tobit Model," Econometrica, Econometric Society, vol. 46(5), pages 1211-1215, September.
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    More about this item

    Keywords

    TESTING ; ECONOMETRICS;

    JEL classification:

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

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