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Robust tests for a linear trend with an application to equity indices

Author

Listed:
  • Astill, Sam
  • Harvey, David I.
  • Leybourne, Stephen J.
  • Taylor, A.M. Robert

Abstract

In this paper we develop a testing procedure for the presence of a deterministic linear trend in a univariate time series which is robust to whether the series is I(0) or I(1) and requires no knowledge of the form of weak dependence present in the data. Our approach is motivated by the testing procedures of Vogelsang [1998, Econometrica, vol 66, p123–148] and Bunzel and Vogelsang [2005, Journal of Business and Economic Statistics, vol 23, p381–394], but utilises an auxiliary unit root test to switch between critical values in the exact I(1) and I(0) environments, rather than using this unit root test to scale the test statistic as is done in the aforementioned procedures. We show that our proposed tests have uniformly greater local asymptotic power than the tests of Vogelsang (1998) and Bunzel and Vogelsang (2005) when the error process is exact I(1), identical local asymptotic when the error process is I(0), and have better overall local asymptotic power when the error process is near I(1). Our proposed tests also display superior finite sample power to the tests of Vogelsang (1998) and Bunzel and Vogelsang (2005) and are competitive in finite samples with tests designed to be optimal in both the exact I(1) and I(0) environments. We apply our test procedures to a number of equity indices and find that these series appear to have a significant upward deterministic trend, yet are also highly persistent about this long run growth path.

Suggested Citation

  • Astill, Sam & Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2014. "Robust tests for a linear trend with an application to equity indices," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 168-185.
    • Handle: RePEc:eee:empfin:v:29:y:2014:i:c:p:168-185
    DOI: 10.1016/j.jempfin.2014.02.004
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    References listed on IDEAS

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    1. Kiefer, Nicholas M. & Vogelsang, Timothy J., 2005. "A New Asymptotic Theory For Heteroskedasticity-Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 21(6), pages 1130-1164, December.
    2. Bunzel, Helle & Vogelsang, Timothy J., 2005. "Powerful Trend Function Tests That Are Robust to Strong Serial Correlation, With an Application to the Prebisch-Singer Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 381-394, October.
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    4. Perron, Pierre & Yabu, Tomoyoshi, 2009. "Estimating deterministic trends with an integrated or stationary noise component," Journal of Econometrics, Elsevier, vol. 151(1), pages 56-69, July.
    5. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2007. "A simple, robust and powerful test of the trend hypothesis," Journal of Econometrics, Elsevier, vol. 141(2), pages 1302-1330, December.
    6. Marsh, Patrick, 2009. "The Properties Of Kullback–Leibler Divergence For The Unit Root Hypothesis," Econometric Theory, Cambridge University Press, vol. 25(6), pages 1662-1681, December.
    7. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    8. Timothy J. Vogelsang, 1998. "Trend Function Hypothesis Testing in the Presence of Serial Correlation," Econometrica, Econometric Society, vol. 66(1), pages 123-148, January.
    9. Anindya Roy & Barry Falk & Wayne A. Fuller, 2004. "Testing for Trend in the Presence of Autoregressive Error," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1082-1091, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Linear trend; Unit root tests; Strong serial correlation;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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