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Robust Testing for Fractional Integration Using the Bootstrap

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 are usually badly sized in small samples, even for normally distributed processes. Furthermore, tests that are well-sized under normality may be severely distorted by non-normalities and ARCH errors. This paper demonstrates how the bootstrap can be implemented to correct for such size distortions. It is shown that a well-designed bootstrap test based on the MRR and GPH tests is exact, and a procedure based on the REG test is nearly exact.

Suggested Citation

  • Andersson, Michael K. & Gredenhoff, Mikael P., 1998. "Robust Testing for Fractional Integration Using the Bootstrap," SSE/EFI Working Paper Series in Economics and Finance 218, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0218
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    References listed on IDEAS

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    2. Horowitz, J.L., 1995. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Working Papers 95-10, University of Iowa, Department of Economics.
    3. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    4. 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.
    5. 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.
    6. Joel L. Horowitz, 1996. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Econometrics 9602009, University Library of Munich, Germany, revised 05 Mar 1996.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

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    2. Hatgioannides, John & Mesomeris, Spyros, 2007. "On the returns generating process and the profitability of trading rules in emerging capital markets," Journal of International Money and Finance, Elsevier, vol. 26(6), pages 948-973, October.
    3. Arteche, Josu & Orbe, Jesus, 2009. "Using the bootstrap for finite sample confidence intervals of the log periodogram regression," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1940-1953, April.
    4. Arteche, J. & Orbe, J., 2005. "Bootstrapping the log-periodogram regression," Economics Letters, Elsevier, vol. 86(1), pages 79-85, January.
    5. Christian de Peretti, 2003. "Bilateral Bootstrap Tests for Long Memory: An Application to the Silver Market," Computational Economics, Springer;Society for Computational Economics, vol. 22(2), pages 187-212, October.
    6. Murphy, A. & Izzeldin, M., 2009. "Bootstrapping long memory tests: Some Monte Carlo results," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2325-2334, April.
    7. Davidson, James, 2002. "A model of fractional cointegration, and tests for cointegration using the bootstrap," Journal of Econometrics, Elsevier, vol. 110(2), pages 187-212, October.
    8. Panas, E., 2001. "Long memory and chaotic models of prices on the London Metal Exchange," Resources Policy, Elsevier, vol. 27(4), pages 235-246, December.

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    More about this item

    Keywords

    Long-memory; resampling; skewness and kurtosis; ARCH; Monte Carlo; size correction.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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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