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Semiparametric and Nonparametric Testing for Long Memory: A Monte Carlo Study

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  • Hauser, Michael A

Abstract

The finite sample properties of three semiparametric estimators, several versions of the modified rescaled range, MRR, and three versions of the GHURST estimator are investigated. Their power and size for testing for long memory under short-run effects, joint shorts and long-run effects, heteroscedasticity and t-distributions are given using Monte Carlo methods. The MRR with the Bartlett window is generally robust with the disadvantage of a relatively small power. The trimmed Whittle likelihood has high power in general and is robust except for large short-run effects. The tests are applied to changes in exchange rate series (daily data) of 6 major countries. The hypothesis of no fractional integration is rejected for none of the series.

Suggested Citation

  • Hauser, Michael A, 1997. "Semiparametric and Nonparametric Testing for Long Memory: A Monte Carlo Study," Empirical Economics, Springer, vol. 22(2), pages 247-271.
  • Handle: RePEc:spr:empeco:v:22:y:1997:i:2:p:247-71
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    Cited by:

    1. Morten Ørregaard Nielsen & Per Houmann Frederiksen, 2005. "Finite Sample Comparison of Parametric, Semiparametric, and Wavelet Estimators of Fractional Integration," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 405-443.
    2. Michael Frömmel & Robinson Kruse, 2012. "Testing for a rational bubble under long memory," Quantitative Finance, Taylor & Francis Journals, vol. 12(11), pages 1723-1732, November.
    3. Castaño Vélez, Elkin & Gallón Gómez, Santiago Alejandro & Gómez Portilla, Karoll, 2011. "Sesgos en estimación, tamaño y potencia de una prueba sobre el parámetro de memoria larga en modelos ARFIMA," REVISTA LECTURAS DE ECONOMÍA, UNIVERSIDAD DE ANTIOQUIA - CIE, February.
    4. Jerry Coakley & Jian Dollery & Neil Kellard, 2011. "Long memory and structural breaks in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(11), pages 1076-1113, November.
    5. Ossama Mikhail & Curtis J. Eberwein & Jagdish Handa, 2003. "Testing and Estimating Persistence in Canadian Unemployment," Econometrics 0311004, EconWPA.
    6. Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus & Teyssiere, Gilles, 2003. "Rescaled variance and related tests for long memory in volatility and levels," Journal of Econometrics, Elsevier, vol. 112(2), pages 265-294, February.
    7. 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.
    8. Joao Sousa Andrade & Irina Syssoyeva-Masson, 2016. "Investigating the presence of long memory in debt series and its relation with growth," EcoMod2016 9627, EcoMod.
    9. Elkin Castaño & Santiago Gallón & Karoll Gómez, 2010. "Estimation Biases, Size and Power of a Test on the Long Memory Parameter in ARFIMA Models," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 73, pages 131-148.
    10. Peters, Andrea & Sibbertsen, Philipp, 2001. "Robust tests on fractional cointegration," Technical Reports 2001,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    11. Sibbertsen, Philipp & Wegener, Christoph & Basse, Tobias, 2014. "Testing for a break in the persistence in yield spreads of EMU government bonds," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 109-118.

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