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Simple (but effective) tests of long memory versus structural breaks

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  • Katsumi Shimotsu

    ()
    (Department of Economics, Queen's University)

Abstract

This paper proposes two simple tests that are based on certain time domain properties of I(d) processes. First, if a time series follows an I(d) process, then each subsample of the time series also follows an I(d) process with the same value of d. Second, if a time series follows an I(d) process, then its dth differenced series follows an I(0) process. Simple as they may sound, these properties provide useful tools to distinguish between true and spurious I(d) processes. In the first test, we split the sample into b subsamples, estimate d for each subsample, and compare them with the estimate of d from the full sample. In the second test, we estimate d, use the estimate to take the dth difference of the sample, and apply the KPSS test and Phillips-Perron test to the differenced data and its partial sum. Both tests are applicable to both stationary and nonstationary I(d) processes. Simulations show that the proposed tests have good power against the spurious long memory models considered in the literature. The tests are applied to the daily realized volatility of the S&P 500 index.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1101.pdf
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Bibliographic Info

Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1101.

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Length: 44 pages
Date of creation: Dec 2006
Date of revision:
Handle: RePEc:qed:wpaper:1101

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Keywords: long memory; fractional integration; structural breaks; realized volatility;

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Citations

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Cited by:
  1. Peter Sephton, 2009. "Critical values for the augmented efficient Wald test for fractional unit roots," Empirical Economics, Springer, vol. 37(3), pages 615-626, December.
  2. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2012. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(2), pages 207-218.
  3. Steven Clark & T. Coggin, 2011. "Are U.S. stock prices mean reverting? Some new tests using fractional integration models with overlapping data and structural breaks," Empirical Economics, Springer, vol. 40(2), pages 373-391, April.
  4. Pierre Perron & Adam McCloskey, 2010. "Memory Parameter Estimation in the Presence of Level Shifts and Deterministic Trends," Boston University - Department of Economics - Working Papers Series WP2010-048, Boston University - Department of Economics.
  5. Qu, Zhongjun, 2011. "A Test Against Spurious Long Memory," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 423-438.
  6. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2009. "Exploring Long Memory and Nonlinearity in Irish Real Exchange Rates using Tests based on Semiparametric Estimation," Working Papers 200901, School Of Economics, University College Dublin.
  7. Niels Haldrup & Robinson Kruse, 2014. "Discriminating between fractional integration and spurious long memory," CREATES Research Papers 2014-19, School of Economics and Management, University of Aarhus.
  8. repec:ebl:ecbull:v:7:y:2007:i:1:p:1-11 is not listed on IDEAS
  9. Sergio Da Silva & Annibal Figueiredo & Iram Gleria & Raul Matsushita, 2007. "Hurst exponents, power laws, and efficiency in the Brazilian foreign exchange market," Economics Bulletin, AccessEcon, vol. 7(1), pages 1-11.
  10. Yohei Yamamoto & Pierre Perron, 2012. "Estimating and Testing Multiple Structural Changes in Linear Models Using Band Spectral Regressions," Global COE Hi-Stat Discussion Paper Series gd12-250, Institute of Economic Research, Hitotsubashi University.
  11. Luisa Bisaglia & Margherita Gerolimetto, 2009. "Testing structural breaks versus long memory with the Box–Pierce statistics: a Monte Carlo study," Statistical Methods and Applications, Springer, vol. 18(4), pages 543-553, November.
  12. Richard T. Baille & Claudio Morana, 2009. "Investigating Inflation Dynamics and Structural Change with an Adaptive ARFIMA Approach," ICER Working Papers - Applied Mathematics Series 06-2009, ICER - International Centre for Economic Research.
  13. Baillie, Richard T. & Morana, Claudio, 2012. "Adaptive ARFIMA models with applications to inflation," Economic Modelling, Elsevier, vol. 29(6), pages 2451-2459.

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