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Some New Tests for a Change in Persistence

Author

Listed:
  • Robert Taylor

    (Department of Economics, University of Birmingham)

  • Stephen Leybourne

    (Department of Economics, University of Nottingham)

Abstract

In this paper we develop new persistence change tests, similar in spirit to those of Kim (2000), Kim et al. (2002) and Busetti and Taylor (2004). While the exisiting tests are based on the maximum over an appropriate sequence of ratios of sub-sample stationarity statistics, our proposed tests are based on the maximum of the sequence of the numerators of these ratios divided by the minimum of the sequence of the denominators of the ratios. The large sample properties of the tests are established, and both finite sample and asymptotic critical values are provided. Numerical evidence suggests that our proposed tests provide a useful complement to the extant tests.

Suggested Citation

  • Robert Taylor & Stephen Leybourne, 2004. "Some New Tests for a Change in Persistence," Economics Bulletin, AccessEcon, vol. 3(39), pages 1-10.
    • Handle: RePEc:ebl:ecbull:eb-04c20028
    as

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    File URL: http://www.accessecon.com/pubs/EB/2004/Volume3/EB-04C20028A.pdf
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    References listed on IDEAS

    as
    1. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    2. Busetti, Fabio & Taylor, A. M. Robert, 2004. "Tests of stationarity against a change in persistence," Journal of Econometrics, Elsevier, vol. 123(1), pages 33-66, November.
    3. Leybourne, Stephen & Taylor, A. M. Robert, 2004. "On tests for changes in persistence," Economics Letters, Elsevier, vol. 84(1), pages 107-115, July.
    4. Kim, Jae-Young & Belaire-Franch, Jorge & Amador, Rosa Badillo, 2002. "Corrigendum to "Detection of change in persistence of a linear time series" [J. Econom. 95 (2000) 97-116]," Journal of Econometrics, Elsevier, vol. 109(2), pages 389-392, August.
    5. Kim, Jae-Young, 2000. "Detection of change in persistence of a linear time series," Journal of Econometrics, Elsevier, vol. 95(1), pages 97-116, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Eiji Kurozumi, 2005. "Detection of Structural Change in the Long‐run Persistence in a Univariate Time Series," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(2), pages 181-206, April.
    2. A. M. Robert Taylor, 2005. "Fluctuation Tests for a Change in Persistence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(2), pages 207-230, April.
    3. Pedro Bação, 2006. "The Performance of Structural Change Tests," Quality & Quantity: International Journal of Methodology, Springer, vol. 40(4), pages 611-628, August.
    4. Petrenko, Victoria (Петренко, ВИктория) & Skrobotov, Anton (Скроботов, Антон) & Turuntseva, Maria (Турунцева, Мария), 2016. "Testing of Changes in Persistence and Their Effect on the Forecasting Quality [Тестирование Изменения Инерционности И Влияние На Качество Прогнозов]," Working Papers 542, Russian Presidential Academy of National Economy and Public Administration.
    5. Jorge Belaire-Franch, 2019. "A note on the evidence of inflation persistence around the world," Empirical Economics, Springer, vol. 56(5), pages 1477-1487, May.
    6. Giorgio Canarella & Rangan Gupta & Stephen M. Miller & Stephen K. Pollard, 2019. "Unemployment rate hysteresis and the great recession: exploring the metropolitan evidence," Empirical Economics, Springer, vol. 56(1), pages 61-79, January.
    7. Li, Fuxiao & Tian, Zheng & Xiao, Yanting & Chen, Zhanshou, 2015. "Variance change-point detection in panel data models," Economics Letters, Elsevier, vol. 126(C), pages 140-143.
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    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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