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Bootstrap Testing for Fractional Integration

Author

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  • Andersson, Michael K.

    (Dept. of Economic Statistics, Stockholm School of Economics)

  • Gredenhoff, Mikael P.

    (Dept. of Economic Statistics, Stockholm School of Economics)

Abstract

Asymptotic tests for fractional integration, such as the Geweke-Porter-Hudak test, the modified rescaled range test and Lagrange multiplier type tests, exhibit size-distortions in small-samples. This paper investigates a parametric bootstrap testing procedure, for size-correction, by means of a computer simulation study. The bootstrap provides a practical method to eliminate size-distortions in the case of an asymptotic pivotal statistic while the power, in general,is close to the corresponding size-adjusted asymptotic test. The results are very encouraging and suggest that a bootstrap testing procedure does correct for size-distortions.

Suggested Citation

  • Andersson, Michael K. & Gredenhoff, Mikael P., 1997. "Bootstrap Testing for Fractional Integration," SSE/EFI Working Paper Series in Economics and Finance 188, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0188
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    References listed on IDEAS

    as
    1. Horowitz, J.L., 1995. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Working Papers 95-10, University of Iowa, Department of Economics.
    2. Diebold, Francis X. & Rudebusch, Glenn D., 1991. "On the power of Dickey-Fuller tests against fractional alternatives," Economics Letters, Elsevier, vol. 35(2), pages 155-160, February.
    3. Yin‐Wong Cheung, 1993. "Tests For Fractional Integration:A Monte Carlo Investigation," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(4), pages 331-345, July.
    4. Joel L. Horowitz, 1996. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Econometrics 9602009, University Library of Munich, Germany, revised 05 Mar 1996.
    5. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    6. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    7. Horowitz, J. L., 1995. "Bootstrap Methods In Econometrics: Theory And Numerical Performance," SFB 373 Discussion Papers 1995,63, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    8. Davidson, Russell & MacKinnon, James G., 1996. "The Power of Bootstrap Tests," Queen's Institute for Economic Research Discussion Papers 273372, Queen's University - Department of Economics.
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    Citations

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    Cited by:

    1. Pilar Grau-Carles, 2005. "Tests of Long Memory: A Bootstrap Approach," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 103-113, February.
    2. Pilar Grau-Carles, 2004. "Test for long memory processes. A bootstrap approach," Computing in Economics and Finance 2004 111, Society for Computational Economics.
    3. Arteche, J. & Orbe, J., 2005. "Bootstrapping the log-periodogram regression," Economics Letters, Elsevier, vol. 86(1), pages 79-85, January.

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    More about this item

    Keywords

    Long-memory; ARFIMA; parametric resampling; small-sample; MonteCarlo simulation; size-correction;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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